Initial commit

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/** filter=lfs diff=lfs merge=lfs -text
+examples/videos/room_video.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/room/images/IMG_1506.HEIC.JPG filter=lfs diff=lfs merge=lfs -text
+skyseg.onnx filter=lfs diff=lfs merge=lfs -text
+examples/ filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.hydra/
+output/
+ckpt/
+dependency/
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Profiling data
+.prof
+
+# Folder specific to your needs
+**/tmp/
+**/outputs/skyseg.onnx

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+-----END CERTIFICATE-----

CODE_OF_CONDUCT.md

@@ -0,0 +1,80 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when there is a
+reasonable belief that an individual's behavior may have a negative impact on
+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <[email protected]>. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

CONTRIBUTING.md

@@ -0,0 +1,31 @@
+# Contributing to vggt
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Facebook's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## License
+By contributing to vggt, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

LICENSE.txt

@@ -0,0 +1,399 @@
+Attribution-NonCommercial 4.0 International
+
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+Section 3 -- License Conditions.
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+Your exercise of the Licensed Rights is expressly made subject to the
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README.md

@@ -0,0 +1,14 @@
+---
+title: vggt
+emoji: 🏆
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.17.1
+app_file: app.py
+pinned: false
+license: cc-by-nc-4.0
+short_description: vggt (alpha test)
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,597 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import cv2
+import torch
+import numpy as np
+import gradio as gr
+import sys
+import shutil
+from datetime import datetime
+import glob
+import gc
+import time
+import spaces
+
+
+sys.path.append("vggt/")
+
+from gradio_util import predictions_to_glb
+from vggt.models.vggt import VGGT
+from vggt.utils.load_fn import load_and_preprocess_images
+from vggt.utils.pose_enc import pose_encoding_to_extri_intri
+from vggt.utils.geometry import unproject_depth_map_to_point_map
+
+
+print("Initializing and loading VGGT model...")
+# model = VGGT.from_pretrained("facebook/VGGT-1B")  # another way to load the model
+
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+model = VGGT()
+_URL = "https://huggingface.co/facebook/VGGT-1B/resolve/main/model.pt"
+model.load_state_dict(torch.hub.load_state_dict_from_url(_URL))
+model.eval()
+# model = model.to(device)
+
+
+# -------------------------------------------------------------------------
+# 1) Core model inference
+# -------------------------------------------------------------------------
+@spaces.GPU(duration=120)
+def run_model(target_dir, model) -> dict:
+    """
+    Run the VGGT model on images in the 'target_dir/images' folder and return predictions.
+    """
+    print(f"Processing images from {target_dir}")
+
+    # Device check
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    if not torch.cuda.is_available():
+        raise ValueError("CUDA is not available. Check your environment.")
+
+    # Move model to device
+    model = model.to(device)
+    model.eval()
+
+    # Load and preprocess images
+    image_names = glob.glob(os.path.join(target_dir, "images", "*"))
+    image_names = sorted(image_names)
+    print(f"Found {len(image_names)} images")
+    if len(image_names) == 0:
+        raise ValueError("No images found. Check your upload.")
+
+    images = load_and_preprocess_images(image_names).to(device)
+    print(f"Preprocessed images shape: {images.shape}")
+
+    # Run inference
+    print("Running inference...")
+    with torch.no_grad():
+        with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+            predictions = model(images)
+
+    # Convert pose encoding to extrinsic and intrinsic matrices
+    print("Converting pose encoding to extrinsic and intrinsic matrices...")
+    extrinsic, intrinsic = pose_encoding_to_extri_intri(predictions["pose_enc"], images.shape[-2:])
+    predictions["extrinsic"] = extrinsic
+    predictions["intrinsic"] = intrinsic
+
+    # Convert tensors to numpy
+    for key in predictions.keys():
+        if isinstance(predictions[key], torch.Tensor):
+            predictions[key] = predictions[key].cpu().numpy().squeeze(0)  # remove batch dimension
+
+    # Generate world points from depth map
+    print("Computing world points from depth map...")
+    depth_map = predictions["depth"]  # (S, H, W, 1)
+    world_points = unproject_depth_map_to_point_map(depth_map, predictions["extrinsic"], predictions["intrinsic"])
+    predictions["world_points_from_depth"] = world_points
+
+    # Clean up
+    torch.cuda.empty_cache()
+    return predictions
+
+
+# -------------------------------------------------------------------------
+# 2) Handle uploaded video/images --> produce target_dir + images
+# -------------------------------------------------------------------------
+def handle_uploads(input_video, input_images):
+    """
+    Create a new 'target_dir' + 'images' subfolder, and place user-uploaded
+    images or extracted frames from video into it. Return (target_dir, image_paths).
+    """
+    start_time = time.time()
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    # Create a unique folder name
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+    target_dir = f"input_images_{timestamp}"
+    target_dir_images = os.path.join(target_dir, "images")
+
+    # Clean up if somehow that folder already exists
+    if os.path.exists(target_dir):
+        shutil.rmtree(target_dir)
+    os.makedirs(target_dir)
+    os.makedirs(target_dir_images)
+
+    image_paths = []
+
+    # --- Handle images ---
+    if input_images is not None:
+        for file_data in input_images:
+            if isinstance(file_data, dict) and "name" in file_data:
+                file_path = file_data["name"]
+            else:
+                file_path = file_data
+            dst_path = os.path.join(target_dir_images, os.path.basename(file_path))
+            shutil.copy(file_path, dst_path)
+            image_paths.append(dst_path)
+
+    # --- Handle video ---
+    if input_video is not None:
+        if isinstance(input_video, dict) and "name" in input_video:
+            video_path = input_video["name"]
+        else:
+            video_path = input_video
+
+        vs = cv2.VideoCapture(video_path)
+        fps = vs.get(cv2.CAP_PROP_FPS)
+        frame_interval = int(fps * 1)  # 1 frame/sec
+
+        count = 0
+        video_frame_num = 0
+        while True:
+            gotit, frame = vs.read()
+            if not gotit:
+                break
+            count += 1
+            if count % frame_interval == 0:
+                image_path = os.path.join(target_dir_images, f"{video_frame_num:06}.png")
+                cv2.imwrite(image_path, frame)
+                image_paths.append(image_path)
+                video_frame_num += 1
+
+    # Sort final images for gallery
+    image_paths = sorted(image_paths)
+
+    end_time = time.time()
+    print(f"Files copied to {target_dir_images}; took {end_time - start_time:.3f} seconds")
+    return target_dir, image_paths
+
+
+# -------------------------------------------------------------------------
+# 3) Update gallery on upload
+# -------------------------------------------------------------------------
+def update_gallery_on_upload(input_video, input_images):
+    """
+    Whenever user uploads or changes files, immediately handle them
+    and show in the gallery. Return (target_dir, image_paths).
+    If nothing is uploaded, returns "None" and empty list.
+    """
+    if not input_video and not input_images:
+        return None, None, None, None
+    target_dir, image_paths = handle_uploads(input_video, input_images)
+    return None, target_dir, image_paths, "Upload complete. Click 'Reconstruct' to begin 3D processing."
+
+
+# -------------------------------------------------------------------------
+# 4) Reconstruction: uses the target_dir plus any viz parameters
+# -------------------------------------------------------------------------
+@spaces.GPU(duration=120)
+def gradio_demo(
+    target_dir,
+    conf_thres=3.0,
+    frame_filter="All",
+    mask_black_bg=False,
+    mask_white_bg=False,
+    show_cam=True,
+    mask_sky=False,
+    prediction_mode="Pointmap Regression",
+):
+    """
+    Perform reconstruction using the already-created target_dir/images.
+    """
+    if not os.path.isdir(target_dir) or target_dir == "None":
+        return None, "No valid target directory found. Please upload first.", None, None
+
+    start_time = time.time()
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    # Prepare frame_filter dropdown
+    target_dir_images = os.path.join(target_dir, "images")
+    all_files = sorted(os.listdir(target_dir_images)) if os.path.isdir(target_dir_images) else []
+    all_files = [f"{i}: {filename}" for i, filename in enumerate(all_files)]
+    frame_filter_choices = ["All"] + all_files
+
+    print("Running run_model...")
+    with torch.no_grad():
+        predictions = run_model(target_dir, model)
+
+    # Save predictions
+    prediction_save_path = os.path.join(target_dir, "predictions.npz")
+    np.savez(prediction_save_path, **predictions)
+
+    # Build a GLB file name
+    glbfile = os.path.join(
+        target_dir,
+        f"glbscene_{conf_thres}_{frame_filter.replace('.', '_').replace(':', '').replace(' ', '_')}_maskb{mask_black_bg}_maskw{mask_white_bg}_cam{show_cam}_sky{mask_sky}_pred{prediction_mode.replace(' ', '_')}.glb",
+    )
+
+    # Convert predictions to GLB
+    glbscene = predictions_to_glb(
+        predictions,
+        conf_thres=conf_thres,
+        filter_by_frames=frame_filter,
+        mask_black_bg=mask_black_bg,
+        mask_white_bg=mask_white_bg,
+        show_cam=show_cam,
+        mask_sky=mask_sky,
+        target_dir=target_dir,
+        prediction_mode=prediction_mode,
+    )
+    glbscene.export(file_obj=glbfile)
+
+    # Cleanup
+    del predictions
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    end_time = time.time()
+    print(f"Total time: {end_time - start_time:.2f} seconds")
+    log_msg = f"Reconstruction Success ({len(all_files)} frames). Waiting for visualization."
+
+    return glbfile, log_msg, gr.Dropdown(choices=frame_filter_choices, value=frame_filter, interactive=True)
+
+
+# -------------------------------------------------------------------------
+# 5) Helper functions for UI resets + re-visualization
+# -------------------------------------------------------------------------
+def clear_fields():
+    """
+    Clears the 3D viewer, the stored target_dir, and empties the gallery.
+    """
+    return None
+
+
+def update_log():
+    """
+    Display a quick log message while waiting.
+    """
+    return "Loading and Reconstructing..."
+
+
+def update_visualization(
+    target_dir, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example
+):
+    """
+    Reload saved predictions from npz, create (or reuse) the GLB for new parameters,
+    and return it for the 3D viewer. If is_example == "True", skip.
+    """
+
+    # If it's an example click, skip as requested
+    if is_example == "True":
+        return None, "No reconstruction available. Please click the Reconstruct button first."
+
+    if not target_dir or target_dir == "None" or not os.path.isdir(target_dir):
+        return None, "No reconstruction available. Please click the Reconstruct button first."
+
+    predictions_path = os.path.join(target_dir, "predictions.npz")
+    if not os.path.exists(predictions_path):
+        return None, f"No reconstruction available at {predictions_path}. Please run 'Reconstruct' first."
+
+    loaded = np.load(predictions_path, allow_pickle=True)
+    predictions = {key: loaded[key] for key in loaded.keys()}
+
+    glbfile = os.path.join(
+        target_dir,
+        f"glbscene_{conf_thres}_{frame_filter.replace('.', '_').replace(':', '').replace(' ', '_')}_maskb{mask_black_bg}_maskw{mask_white_bg}_cam{show_cam}_sky{mask_sky}_pred{prediction_mode.replace(' ', '_')}.glb",
+    )
+
+    if not os.path.exists(glbfile):
+        glbscene = predictions_to_glb(
+            predictions,
+            conf_thres=conf_thres,
+            filter_by_frames=frame_filter,
+            mask_black_bg=mask_black_bg,
+            mask_white_bg=mask_white_bg,
+            show_cam=show_cam,
+            mask_sky=mask_sky,
+            target_dir=target_dir,
+            prediction_mode=prediction_mode,
+        )
+        glbscene.export(file_obj=glbfile)
+
+    return glbfile, "Updating Visualization"
+
+
+# -------------------------------------------------------------------------
+# Example images
+# -------------------------------------------------------------------------
+
+canyon_video = "examples/videos/Studlagil_Canyon_East_Iceland.mp4"
+great_wall_video = "examples/videos/great_wall.mp4"
+colosseum_video = "examples/videos/Colosseum.mp4"
+room_video = "examples/videos/room.mp4"
+kitchen_video = "examples/videos/kitchen.mp4"
+fern_video = "examples/videos/fern.mp4"
+single_cartoon_video = "examples/videos/single_cartoon.mp4"
+single_oil_painting_video = "examples/videos/single_oil_painting.mp4"
+pyramid_video = "examples/videos/pyramid.mp4"
+
+
+# -------------------------------------------------------------------------
+# 6) Build Gradio UI
+# -------------------------------------------------------------------------
+theme = gr.themes.Ocean()
+theme.set(
+    checkbox_label_background_fill_selected="*button_primary_background_fill",
+    checkbox_label_text_color_selected="*button_primary_text_color",
+)
+
+with gr.Blocks(
+    theme=theme,
+    css="""
+    .custom-log * {
+        font-style: italic;
+        font-size: 22px !important;
+        background-image: linear-gradient(120deg, #0ea5e9 0%, #6ee7b7 60%, #34d399 100%);
+        -webkit-background-clip: text;
+        background-clip: text;
+        font-weight: bold !important;
+        color: transparent !important;
+        text-align: center !important;
+    }
+    
+    .example-log * {
+        font-style: italic;
+        font-size: 16px !important;
+        background-image: linear-gradient(120deg, #0ea5e9 0%, #6ee7b7 60%, #34d399 100%);
+        -webkit-background-clip: text;
+        background-clip: text;
+        color: transparent !important;
+    }
+    
+    #my_radio .wrap {
+        display: flex;
+        flex-wrap: nowrap;
+        justify-content: center;
+        align-items: center;
+    }
+
+    #my_radio .wrap label {
+        display: flex;
+        width: 50%;
+        justify-content: center;
+        align-items: center;
+        margin: 0;
+        padding: 10px 0;
+        box-sizing: border-box;
+    }
+    """,
+) as demo:
+
+    # Instead of gr.State, we use a hidden Textbox:
+    is_example = gr.Textbox(label="is_example", visible=False, value="None")
+    num_images = gr.Textbox(label="num_images", visible=False, value="None")
+    
+    gr.Markdown(
+        """
+    # 🏛️ VGGT: Visual Geometry Grounded Transformer
+
+    [🐙 GitHub Repository](https://github.com/facebookresearch/vggt) | [Project Page]()
+
+    <div style="font-size: 16px; line-height: 1.5;">
+    <p>Upload a video or a set of images to create a 3D reconstruction of a scene or object.  VGGT takes these images and generates a 3D point cloud, along with estimated camera poses.</p>
+
+    <h3>Getting Started:</h3>
+    <ol>
+        <li><strong>Upload Your Data:</strong> Use the "Upload Video" or "Upload Images" buttons on the left to provide your input.  Videos will be automatically split into individual frames (one frame per second).</li>
+        <li><strong>Preview:</strong>  Your uploaded images will appear in the gallery on the left.</li>
+        <li><strong>Reconstruct:</strong> Click the "Reconstruct" button to start the 3D reconstruction process.</li>
+        <li><strong>Visualize:</strong>  The 3D reconstruction will appear in the viewer on the right.  You can rotate, pan, and zoom to explore the model, and download the GLB file. Note the visualization of 3D points may be slow for large number of input images. </li>
+        <li><strong>Adjust Visualization (Optional):</strong> After reconstruction, you can fine-tune the visualization using the options below:
+            <ul>
+                <li><em>Confidence Threshold:</em>  Adjusts the filtering of points based on the model's confidence.  Higher values show only the most confident points.</li>
+                <li><em>Show Points from Frame:</em> Select specific frames to display in the point cloud.  Useful for isolating parts of a scene.</li>
+                <li><em>Show Camera:</em> Toggle the display of the estimated camera positions.</li>
+                <li><em>Filter Sky / Filter Black Background:</em>  These options attempt to remove points corresponding to the sky or black backgrounds.</li>
+                <li><em>Select a Prediction Mode:</em> Choose between "Depthmap and Camera Branch" and "Pointmap Branch". They usually look similar, while "Depthmap and Camera Branch" give slightly better details.</li>
+            </ul>
+        </li>
+    </ol>
+    <p><strong>Please note:</strong> Our method usually only needs less than 1 second to reconstruct a scene, but the visualization of 3D points may take tens of seconds, especially when the number of images is large. Please be patient or, for faster visualization, use a local machine to run our demo from our <a href="https://github.com/facebookresearch/vggt">GitHub repository</a>.</p>
+    </div>
+    """
+    )
+
+    target_dir_output = gr.Textbox(label="Target Dir", visible=False, value="None")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            input_video = gr.Video(label="Upload Video", interactive=True)
+            input_images = gr.File(file_count="multiple", label="Upload Images", interactive=True)
+
+            image_gallery = gr.Gallery(
+                label="Preview",
+                columns=4,
+                height="300px",
+                show_download_button=True,
+                object_fit="contain",
+                preview=True,
+            )
+
+        with gr.Column(scale=4):
+            with gr.Column():
+                gr.Markdown("**3D Reconstruction (Point Cloud and Camera Poses)**")
+                log_output = gr.Markdown(
+                    "Please upload a video or images, then click Reconstruct.", elem_classes=["custom-log"]
+                )
+                reconstruction_output = gr.Model3D(height=520, zoom_speed=0.5, pan_speed=0.5)
+
+            with gr.Row():
+                submit_btn = gr.Button("Reconstruct", scale=1, variant="primary")
+                clear_btn = gr.ClearButton(
+                    [input_video, input_images, reconstruction_output, log_output, target_dir_output, image_gallery],
+                    scale=1,
+                )
+
+            with gr.Row():
+                prediction_mode = gr.Radio(
+                    ["Depthmap and Camera Branch", "Pointmap Branch"],
+                    label="Select a Prediction Mode",
+                    value="Depthmap and Camera Branch",
+                    scale=1,
+                    elem_id="my_radio",
+                )
+
+            with gr.Row():
+                conf_thres = gr.Slider(minimum=0, maximum=100, value=50, step=0.1, label="Confidence Threshold (%)")
+                frame_filter = gr.Dropdown(choices=["All"], value="All", label="Show Points from Frame")
+                with gr.Column():
+                    show_cam = gr.Checkbox(label="Show Camera", value=True)
+                    mask_sky = gr.Checkbox(label="Filter Sky", value=False)
+                    mask_black_bg = gr.Checkbox(label="Filter Black Background", value=False)
+                    mask_white_bg = gr.Checkbox(label="Filter White Background", value=False)
+
+    # ---------------------- Examples section ----------------------
+    examples = [
+        [colosseum_video, "22", None, 20.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+        [pyramid_video, "30", None, 35.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+        [single_cartoon_video, "1", None, 15.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+        [single_oil_painting_video, "1", None, 20.0, False, True, True, True, "Depthmap and Camera Branch", "True"],
+        [canyon_video, "14", None, 40.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+        [room_video, "8", None, 5.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+        [kitchen_video, "25", None, 50.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+        [fern_video, "20", None, 45.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+    ]
+
+    def example_pipeline(
+        input_video,
+        num_images_str,
+        input_images,
+        conf_thres,
+        mask_black_bg,
+        mask_white_bg,
+        show_cam,
+        mask_sky,
+        prediction_mode,
+        is_example_str,
+    ):
+        """
+        1) Copy example images to new target_dir
+        2) Reconstruct
+        3) Return model3D + logs + new_dir + updated dropdown + gallery
+        We do NOT return is_example. It's just an input.
+        """
+        target_dir, image_paths = handle_uploads(input_video, input_images)
+        # Always use "All" for frame_filter in examples
+        frame_filter = "All"
+        glbfile, log_msg, dropdown = gradio_demo(
+            target_dir, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode
+        )
+        return glbfile, log_msg, target_dir, dropdown, image_paths
+
+    gr.Markdown("Click any row to load an example.", elem_classes=["example-log"])
+
+    gr.Examples(
+        examples=examples,
+        inputs=[
+            input_video,
+            num_images,
+            input_images,
+            conf_thres,
+            mask_black_bg,
+            mask_white_bg,
+            show_cam,
+            mask_sky,
+            prediction_mode,
+            is_example,
+        ],
+        outputs=[
+            reconstruction_output,
+            log_output,
+            target_dir_output,
+            frame_filter,
+            image_gallery,
+        ],
+        fn=example_pipeline,
+        cache_examples=False,
+        examples_per_page=50,
+    )
+
+    # -------------------------------------------------------------------------
+    # "Reconstruct" button logic:
+    #  - Clear fields
+    #  - Update log
+    #  - gradio_demo(...) with the existing target_dir
+    #  - Then set is_example = "False"
+    # -------------------------------------------------------------------------
+    submit_btn.click(fn=clear_fields, inputs=[], outputs=[reconstruction_output]).then(
+        fn=update_log, inputs=[], outputs=[log_output]
+    ).then(
+        fn=gradio_demo,
+        inputs=[target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode],
+        outputs=[reconstruction_output, log_output, frame_filter],
+    ).then(
+        fn=lambda: "False", inputs=[], outputs=[is_example]  # set is_example to "False"
+    )
+
+    # -------------------------------------------------------------------------
+    # Real-time Visualization Updates
+    # -------------------------------------------------------------------------
+    conf_thres.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+    frame_filter.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+    mask_black_bg.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+    mask_white_bg.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+    show_cam.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+    mask_sky.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+    prediction_mode.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+
+    # -------------------------------------------------------------------------
+    # Auto-update gallery whenever user uploads or changes their files
+    # -------------------------------------------------------------------------
+    input_video.change(
+        fn=update_gallery_on_upload,
+        inputs=[input_video, input_images],
+        outputs=[reconstruction_output, target_dir_output, image_gallery, log_output],
+    )
+    input_images.change(
+        fn=update_gallery_on_upload,
+        inputs=[input_video, input_images],
+        outputs=[reconstruction_output, target_dir_output, image_gallery, log_output],
+    )
+
+    demo.queue(max_size=20).launch(show_error=True, share=True)

demo_gradio.py

@@ -0,0 +1,593 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import cv2
+import torch
+import numpy as np
+import gradio as gr
+import sys
+import shutil
+from datetime import datetime
+import glob
+import gc
+import time
+
+sys.path.append("vggt/")
+
+from gradio_util import predictions_to_glb
+from vggt.models.vggt import VGGT
+from vggt.utils.load_fn import load_and_preprocess_images
+from vggt.utils.pose_enc import pose_encoding_to_extri_intri
+from vggt.utils.geometry import unproject_depth_map_to_point_map
+
+
+print("Initializing and loading VGGT model...")
+# model = VGGT.from_pretrained("facebook/VGGT-1B")  # another way to load the model
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = VGGT()
+_URL = "https://huggingface.co/facebook/VGGT-1B/resolve/main/model.pt"
+model.load_state_dict(torch.hub.load_state_dict_from_url(_URL))
+model.eval()
+model = model.to(device)
+
+
+# -------------------------------------------------------------------------
+# 1) Core model inference
+# -------------------------------------------------------------------------
+def run_model(target_dir, model) -> dict:
+    """
+    Run the VGGT model on images in the 'target_dir/images' folder and return predictions.
+    """
+    print(f"Processing images from {target_dir}")
+
+    # Device check
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    if not torch.cuda.is_available():
+        raise ValueError("CUDA is not available. Check your environment.")
+
+    # Move model to device
+    model = model.to(device)
+    model.eval()
+
+    # Load and preprocess images
+    image_names = glob.glob(os.path.join(target_dir, "images", "*"))
+    image_names = sorted(image_names)
+    print(f"Found {len(image_names)} images")
+    if len(image_names) == 0:
+        raise ValueError("No images found. Check your upload.")
+
+    images = load_and_preprocess_images(image_names).to(device)
+    print(f"Preprocessed images shape: {images.shape}")
+
+    # Run inference
+    print("Running inference...")
+    with torch.no_grad():
+        with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+            predictions = model(images)
+
+    # Convert pose encoding to extrinsic and intrinsic matrices
+    print("Converting pose encoding to extrinsic and intrinsic matrices...")
+    extrinsic, intrinsic = pose_encoding_to_extri_intri(predictions["pose_enc"], images.shape[-2:])
+    predictions["extrinsic"] = extrinsic
+    predictions["intrinsic"] = intrinsic
+
+    # Convert tensors to numpy
+    for key in predictions.keys():
+        if isinstance(predictions[key], torch.Tensor):
+            predictions[key] = predictions[key].cpu().numpy().squeeze(0)  # remove batch dimension
+
+    # Generate world points from depth map
+    print("Computing world points from depth map...")
+    depth_map = predictions["depth"]  # (S, H, W, 1)
+    world_points = unproject_depth_map_to_point_map(depth_map, predictions["extrinsic"], predictions["intrinsic"])
+    predictions["world_points_from_depth"] = world_points
+
+    # Clean up
+    torch.cuda.empty_cache()
+    return predictions
+
+
+# -------------------------------------------------------------------------
+# 2) Handle uploaded video/images --> produce target_dir + images
+# -------------------------------------------------------------------------
+def handle_uploads(input_video, input_images):
+    """
+    Create a new 'target_dir' + 'images' subfolder, and place user-uploaded
+    images or extracted frames from video into it. Return (target_dir, image_paths).
+    """
+    start_time = time.time()
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    # Create a unique folder name
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+    target_dir = f"input_images_{timestamp}"
+    target_dir_images = os.path.join(target_dir, "images")
+
+    # Clean up if somehow that folder already exists
+    if os.path.exists(target_dir):
+        shutil.rmtree(target_dir)
+    os.makedirs(target_dir)
+    os.makedirs(target_dir_images)
+
+    image_paths = []
+
+    # --- Handle images ---
+    if input_images is not None:
+        for file_data in input_images:
+            if isinstance(file_data, dict) and "name" in file_data:
+                file_path = file_data["name"]
+            else:
+                file_path = file_data
+            dst_path = os.path.join(target_dir_images, os.path.basename(file_path))
+            shutil.copy(file_path, dst_path)
+            image_paths.append(dst_path)
+
+    # --- Handle video ---
+    if input_video is not None:
+        if isinstance(input_video, dict) and "name" in input_video:
+            video_path = input_video["name"]
+        else:
+            video_path = input_video
+
+        vs = cv2.VideoCapture(video_path)
+        fps = vs.get(cv2.CAP_PROP_FPS)
+        frame_interval = int(fps * 1)  # 1 frame/sec
+
+        count = 0
+        video_frame_num = 0
+        while True:
+            gotit, frame = vs.read()
+            if not gotit:
+                break
+            count += 1
+            if count % frame_interval == 0:
+                image_path = os.path.join(target_dir_images, f"{video_frame_num:06}.png")
+                cv2.imwrite(image_path, frame)
+                image_paths.append(image_path)
+                video_frame_num += 1
+
+    # Sort final images for gallery
+    image_paths = sorted(image_paths)
+
+    end_time = time.time()
+    print(f"Files copied to {target_dir_images}; took {end_time - start_time:.3f} seconds")
+    return target_dir, image_paths
+
+
+# -------------------------------------------------------------------------
+# 3) Update gallery on upload
+# -------------------------------------------------------------------------
+def update_gallery_on_upload(input_video, input_images):
+    """
+    Whenever user uploads or changes files, immediately handle them
+    and show in the gallery. Return (target_dir, image_paths).
+    If nothing is uploaded, returns "None" and empty list.
+    """
+    if not input_video and not input_images:
+        return None, None, None, None
+    target_dir, image_paths = handle_uploads(input_video, input_images)
+    return None, target_dir, image_paths, "Upload complete. Click 'Reconstruct' to begin 3D processing."
+
+
+# -------------------------------------------------------------------------
+# 4) Reconstruction: uses the target_dir plus any viz parameters
+# -------------------------------------------------------------------------
+def gradio_demo(
+    target_dir,
+    conf_thres=3.0,
+    frame_filter="All",
+    mask_black_bg=False,
+    mask_white_bg=False,
+    show_cam=True,
+    mask_sky=False,
+    prediction_mode="Pointmap Regression",
+):
+    """
+    Perform reconstruction using the already-created target_dir/images.
+    """
+    if not os.path.isdir(target_dir) or target_dir == "None":
+        return None, "No valid target directory found. Please upload first.", None, None
+
+    start_time = time.time()
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    # Prepare frame_filter dropdown
+    target_dir_images = os.path.join(target_dir, "images")
+    all_files = sorted(os.listdir(target_dir_images)) if os.path.isdir(target_dir_images) else []
+    all_files = [f"{i}: {filename}" for i, filename in enumerate(all_files)]
+    frame_filter_choices = ["All"] + all_files
+
+    print("Running run_model...")
+    with torch.no_grad():
+        predictions = run_model(target_dir, model)
+
+    # Save predictions
+    prediction_save_path = os.path.join(target_dir, "predictions.npz")
+    np.savez(prediction_save_path, **predictions)
+
+    # Build a GLB file name
+    glbfile = os.path.join(
+        target_dir,
+        f"glbscene_{conf_thres}_{frame_filter.replace('.', '_').replace(':', '').replace(' ', '_')}_maskb{mask_black_bg}_maskw{mask_white_bg}_cam{show_cam}_sky{mask_sky}_pred{prediction_mode.replace(' ', '_')}.glb",
+    )
+
+    # Convert predictions to GLB
+    glbscene = predictions_to_glb(
+        predictions,
+        conf_thres=conf_thres,
+        filter_by_frames=frame_filter,
+        mask_black_bg=mask_black_bg,
+        mask_white_bg=mask_white_bg,
+        show_cam=show_cam,
+        mask_sky=mask_sky,
+        target_dir=target_dir,
+        prediction_mode=prediction_mode,
+    )
+    glbscene.export(file_obj=glbfile)
+
+    # Cleanup
+    del predictions
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    end_time = time.time()
+    print(f"Total time: {end_time - start_time:.2f} seconds")
+    log_msg = f"Reconstruction Success ({len(all_files)} frames). Waiting for visualization."
+
+    return glbfile, log_msg, gr.Dropdown(choices=frame_filter_choices, value=frame_filter, interactive=True)
+
+
+# -------------------------------------------------------------------------
+# 5) Helper functions for UI resets + re-visualization
+# -------------------------------------------------------------------------
+def clear_fields():
+    """
+    Clears the 3D viewer, the stored target_dir, and empties the gallery.
+    """
+    return None
+
+
+def update_log():
+    """
+    Display a quick log message while waiting.
+    """
+    return "Loading and Reconstructing..."
+
+
+def update_visualization(
+    target_dir, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example
+):
+    """
+    Reload saved predictions from npz, create (or reuse) the GLB for new parameters,
+    and return it for the 3D viewer. If is_example == "True", skip.
+    """
+
+    # If it's an example click, skip as requested
+    if is_example == "True":
+        return None, "No reconstruction available. Please click the Reconstruct button first."
+
+    if not target_dir or target_dir == "None" or not os.path.isdir(target_dir):
+        return None, "No reconstruction available. Please click the Reconstruct button first."
+
+    predictions_path = os.path.join(target_dir, "predictions.npz")
+    if not os.path.exists(predictions_path):
+        return None, f"No reconstruction available at {predictions_path}. Please run 'Reconstruct' first."
+
+    loaded = np.load(predictions_path, allow_pickle=True)
+    predictions = {key: loaded[key] for key in loaded.keys()}
+
+    glbfile = os.path.join(
+        target_dir,
+        f"glbscene_{conf_thres}_{frame_filter.replace('.', '_').replace(':', '').replace(' ', '_')}_maskb{mask_black_bg}_maskw{mask_white_bg}_cam{show_cam}_sky{mask_sky}_pred{prediction_mode.replace(' ', '_')}.glb",
+    )
+
+    if not os.path.exists(glbfile):
+        glbscene = predictions_to_glb(
+            predictions,
+            conf_thres=conf_thres,
+            filter_by_frames=frame_filter,
+            mask_black_bg=mask_black_bg,
+            mask_white_bg=mask_white_bg,
+            show_cam=show_cam,
+            mask_sky=mask_sky,
+            target_dir=target_dir,
+            prediction_mode=prediction_mode,
+        )
+        glbscene.export(file_obj=glbfile)
+
+    return glbfile, "Updating Visualization"
+
+
+# -------------------------------------------------------------------------
+# Example images
+# -------------------------------------------------------------------------
+
+canyon_video = "examples/videos/Studlagil_Canyon_East_Iceland.mp4"
+great_wall_video = "examples/videos/great_wall.mp4"
+colosseum_video = "examples/videos/Colosseum.mp4"
+room_video = "examples/videos/room.mp4"
+kitchen_video = "examples/videos/kitchen.mp4"
+fern_video = "examples/videos/fern.mp4"
+single_cartoon_video = "examples/videos/single_cartoon.mp4"
+single_oil_painting_video = "examples/videos/single_oil_painting.mp4"
+pyramid_video = "examples/videos/pyramid.mp4"
+
+
+# -------------------------------------------------------------------------
+# 6) Build Gradio UI
+# -------------------------------------------------------------------------
+theme = gr.themes.Ocean()
+theme.set(
+    checkbox_label_background_fill_selected="*button_primary_background_fill",
+    checkbox_label_text_color_selected="*button_primary_text_color",
+)
+
+with gr.Blocks(
+    theme=theme,
+    css="""
+    .custom-log * {
+        font-style: italic;
+        font-size: 22px !important;
+        background-image: linear-gradient(120deg, #0ea5e9 0%, #6ee7b7 60%, #34d399 100%);
+        -webkit-background-clip: text;
+        background-clip: text;
+        font-weight: bold !important;
+        color: transparent !important;
+        text-align: center !important;
+    }
+    
+    .example-log * {
+        font-style: italic;
+        font-size: 16px !important;
+        background-image: linear-gradient(120deg, #0ea5e9 0%, #6ee7b7 60%, #34d399 100%);
+        -webkit-background-clip: text;
+        background-clip: text;
+        color: transparent !important;
+    }
+    
+    #my_radio .wrap {
+        display: flex;
+        flex-wrap: nowrap;
+        justify-content: center;
+        align-items: center;
+    }
+
+    #my_radio .wrap label {
+        display: flex;
+        width: 50%;
+        justify-content: center;
+        align-items: center;
+        margin: 0;
+        padding: 10px 0;
+        box-sizing: border-box;
+    }
+    """,
+) as demo:
+
+    # Instead of gr.State, we use a hidden Textbox:
+    is_example = gr.Textbox(label="is_example", visible=False, value="None")
+    num_images = gr.Textbox(label="num_images", visible=False, value="None")
+    
+    gr.Markdown(
+        """
+    # 🏛️ VGGT: Visual Geometry Grounded Transformer
+
+    [🐙 GitHub Repository](https://github.com/facebookresearch/vggt) | [Project Page]()
+
+    <div style="font-size: 16px; line-height: 1.5;">
+    <p>Upload a video or a set of images to create a 3D reconstruction of a scene or object.  VGGT takes these images and generates a 3D point cloud, along with estimated camera poses.</p>
+
+    <h3>Getting Started:</h3>
+    <ol>
+        <li><strong>Upload Your Data:</strong> Use the "Upload Video" or "Upload Images" buttons on the left to provide your input.  Videos will be automatically split into individual frames (one frame per second).</li>
+        <li><strong>Preview:</strong>  Your uploaded images will appear in the gallery on the left.</li>
+        <li><strong>Reconstruct:</strong> Click the "Reconstruct" button to start the 3D reconstruction process.</li>
+        <li><strong>Visualize:</strong>  The 3D reconstruction will appear in the viewer on the right.  You can rotate, pan, and zoom to explore the model, and download the GLB file. Note the visualization of 3D points may be slow for large number of input images. </li>
+        <li><strong>Adjust Visualization (Optional):</strong> After reconstruction, you can fine-tune the visualization using the options below:
+            <ul>
+                <li><em>Confidence Threshold:</em>  Adjusts the filtering of points based on the model's confidence.  Higher values show only the most confident points.</li>
+                <li><em>Show Points from Frame:</em> Select specific frames to display in the point cloud.  Useful for isolating parts of a scene.</li>
+                <li><em>Show Camera:</em> Toggle the display of the estimated camera positions.</li>
+                <li><em>Filter Sky / Filter Black Background:</em>  These options attempt to remove points corresponding to the sky or black backgrounds.</li>
+                <li><em>Select a Prediction Mode:</em> Choose between "Depthmap and Camera Branch" and "Pointmap Branch". They usually look similar, while "Depthmap and Camera Branch" give slightly better details.</li>
+            </ul>
+        </li>
+    </ol>
+    <p><strong>Please note:</strong> Our method usually only needs less than 1 second to reconstruct a scene, but the visualization of 3D points may take tens of seconds, especially when the number of images is large. Please be patient or, for faster visualization, use a local machine to run our demo from our <a href="https://github.com/facebookresearch/vggt">GitHub repository</a>.</p>
+    </div>
+    """
+    )
+
+    target_dir_output = gr.Textbox(label="Target Dir", visible=False, value="None")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            input_video = gr.Video(label="Upload Video", interactive=True)
+            input_images = gr.File(file_count="multiple", label="Upload Images", interactive=True)
+
+            image_gallery = gr.Gallery(
+                label="Preview",
+                columns=4,
+                height="300px",
+                show_download_button=True,
+                object_fit="contain",
+                preview=True,
+            )
+
+        with gr.Column(scale=4):
+            with gr.Column():
+                gr.Markdown("**3D Reconstruction (Point Cloud and Camera Poses)**")
+                log_output = gr.Markdown(
+                    "Please upload a video or images, then click Reconstruct.", elem_classes=["custom-log"]
+                )
+                reconstruction_output = gr.Model3D(height=520, zoom_speed=0.5, pan_speed=0.5)
+
+            with gr.Row():
+                submit_btn = gr.Button("Reconstruct", scale=1, variant="primary")
+                clear_btn = gr.ClearButton(
+                    [input_video, input_images, reconstruction_output, log_output, target_dir_output, image_gallery],
+                    scale=1,
+                )
+
+            with gr.Row():
+                prediction_mode = gr.Radio(
+                    ["Depthmap and Camera Branch", "Pointmap Branch"],
+                    label="Select a Prediction Mode",
+                    value="Depthmap and Camera Branch",
+                    scale=1,
+                    elem_id="my_radio",
+                )
+
+            with gr.Row():
+                conf_thres = gr.Slider(minimum=0, maximum=100, value=50, step=0.1, label="Confidence Threshold (%)")
+                frame_filter = gr.Dropdown(choices=["All"], value="All", label="Show Points from Frame")
+                with gr.Column():
+                    show_cam = gr.Checkbox(label="Show Camera", value=True)
+                    mask_sky = gr.Checkbox(label="Filter Sky", value=False)
+                    mask_black_bg = gr.Checkbox(label="Filter Black Background", value=False)
+                    mask_white_bg = gr.Checkbox(label="Filter White Background", value=False)
+
+    # ---------------------- Examples section ----------------------
+    examples = [
+        [colosseum_video, "22", None, 20.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+        [pyramid_video, "30", None, 35.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+        [single_cartoon_video, "1", None, 15.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+        [single_oil_painting_video, "1", None, 20.0, False, True, True, True, "Depthmap and Camera Branch", "True"],
+        [canyon_video, "14", None, 40.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+        [room_video, "8", None, 5.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+        [kitchen_video, "25", None, 50.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+        [fern_video, "20", None, 45.0, False, False, True, False, "Depthmap and Camera Branch", "True"],
+    ]
+
+    def example_pipeline(
+        input_video,
+        num_images_str,
+        input_images,
+        conf_thres,
+        mask_black_bg,
+        mask_white_bg,
+        show_cam,
+        mask_sky,
+        prediction_mode,
+        is_example_str,
+    ):
+        """
+        1) Copy example images to new target_dir
+        2) Reconstruct
+        3) Return model3D + logs + new_dir + updated dropdown + gallery
+        We do NOT return is_example. It's just an input.
+        """
+        target_dir, image_paths = handle_uploads(input_video, input_images)
+        # Always use "All" for frame_filter in examples
+        frame_filter = "All"
+        glbfile, log_msg, dropdown = gradio_demo(
+            target_dir, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode
+        )
+        return glbfile, log_msg, target_dir, dropdown, image_paths
+
+    gr.Markdown("Click any row to load an example.", elem_classes=["example-log"])
+
+    gr.Examples(
+        examples=examples,
+        inputs=[
+            input_video,
+            num_images,
+            input_images,
+            conf_thres,
+            mask_black_bg,
+            mask_white_bg,
+            show_cam,
+            mask_sky,
+            prediction_mode,
+            is_example,
+        ],
+        outputs=[
+            reconstruction_output,
+            log_output,
+            target_dir_output,
+            frame_filter,
+            image_gallery,
+        ],
+        fn=example_pipeline,
+        cache_examples=False,
+        examples_per_page=50,
+    )
+
+    # -------------------------------------------------------------------------
+    # "Reconstruct" button logic:
+    #  - Clear fields
+    #  - Update log
+    #  - gradio_demo(...) with the existing target_dir
+    #  - Then set is_example = "False"
+    # -------------------------------------------------------------------------
+    submit_btn.click(fn=clear_fields, inputs=[], outputs=[reconstruction_output]).then(
+        fn=update_log, inputs=[], outputs=[log_output]
+    ).then(
+        fn=gradio_demo,
+        inputs=[target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode],
+        outputs=[reconstruction_output, log_output, frame_filter],
+    ).then(
+        fn=lambda: "False", inputs=[], outputs=[is_example]  # set is_example to "False"
+    )
+
+    # -------------------------------------------------------------------------
+    # Real-time Visualization Updates
+    # -------------------------------------------------------------------------
+    conf_thres.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+    frame_filter.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+    mask_black_bg.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+    mask_white_bg.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+    show_cam.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+    mask_sky.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+    prediction_mode.change(
+        update_visualization,
+        [target_dir_output, conf_thres, frame_filter, mask_black_bg, mask_white_bg, show_cam, mask_sky, prediction_mode, is_example],
+        [reconstruction_output, log_output],
+    )
+
+    # -------------------------------------------------------------------------
+    # Auto-update gallery whenever user uploads or changes their files
+    # -------------------------------------------------------------------------
+    input_video.change(
+        fn=update_gallery_on_upload,
+        inputs=[input_video, input_images],
+        outputs=[reconstruction_output, target_dir_output, image_gallery, log_output],
+    )
+    input_images.change(
+        fn=update_gallery_on_upload,
+        inputs=[input_video, input_images],
+        outputs=[reconstruction_output, target_dir_output, image_gallery, log_output],
+    )
+
+    demo.queue(max_size=20).launch(show_error=True, share=True)

demo_viser.py

@@ -0,0 +1,506 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import glob
+import time
+import threading
+import argparse
+from typing import List, Optional
+import copy
+
+import numpy as np
+import torch
+from tqdm.auto import tqdm
+import viser
+import viser.transforms as viser_tf
+import cv2
+import requests
+try:
+    import onnxruntime
+except ImportError:
+    print("onnxruntime not found. Sky segmentation may not work.")
+
+from vggt.models.vggt import VGGT
+from vggt.utils.load_fn import load_and_preprocess_images
+from vggt.utils.geometry import closed_form_inverse_se3, unproject_depth_map_to_point_map
+from vggt.utils.pose_enc import pose_encoding_to_extri_intri
+
+def viser_wrapper(
+    pred_dict: dict,
+    port: int = 8080,
+    init_conf_threshold: float = 50.0,  # represents percentage (e.g., 50 means filter lowest 50%)
+    use_point_map: bool = False,
+    background_mode: bool = False,
+    mask_sky: bool = False,
+    image_folder: str = None,
+):
+    """
+    Visualize predicted 3D points and camera poses with viser.
+
+    Args:
+        pred_dict (dict):
+            {
+                "images": (S, 3, H, W)   - Input images,
+                "world_points": (S, H, W, 3),
+                "world_points_conf": (S, H, W),
+                "depth": (S, H, W, 1),
+                "depth_conf": (S, H, W),
+                "extrinsic": (S, 3, 4),
+                "intrinsic": (S, 3, 3),
+            }
+        port (int): Port number for the viser server.
+        init_conf_threshold (float): Initial percentage of low-confidence points to filter out.
+        use_point_map (bool): Whether to visualize world_points or use depth-based points.
+        background_mode (bool): Whether to run the server in background thread.
+        mask_sky (bool): Whether to apply sky segmentation to filter out sky points.
+        image_folder (str): Path to the folder containing input images.
+    """
+    print(f"Starting viser server on port {port}")
+
+    server = viser.ViserServer(host="0.0.0.0", port=port)
+    server.gui.configure_theme(titlebar_content=None, control_layout="collapsible")
+
+    # Unpack prediction dict
+    images = pred_dict["images"]  # (S, 3, H, W)
+    world_points_map = pred_dict["world_points"]  # (S, H, W, 3)
+    conf_map = pred_dict["world_points_conf"]  # (S, H, W)
+
+    depth_map = pred_dict["depth"]  # (S, H, W, 1)
+    depth_conf = pred_dict["depth_conf"]  # (S, H, W)
+
+    extrinsics_cam = pred_dict["extrinsic"]  # (S, 3, 4)
+    intrinsics_cam = pred_dict["intrinsic"]  # (S, 3, 3)
+
+    # Compute world points from depth if not using the precomputed point map
+    if not use_point_map:
+        world_points = unproject_depth_map_to_point_map(depth_map, extrinsics_cam, intrinsics_cam)
+        conf = depth_conf
+    else:
+        world_points = world_points_map
+        conf = conf_map
+
+    # Apply sky segmentation if enabled
+    if mask_sky and image_folder is not None:
+        conf = apply_sky_segmentation(conf, image_folder)
+
+    # Convert images from (S, 3, H, W) to (S, H, W, 3)
+    # Then flatten everything for the point cloud
+    colors = images.transpose(0, 2, 3, 1)  # now (S, H, W, 3)
+    S, H, W, _ = world_points.shape
+
+    # Flatten
+    points = world_points.reshape(-1, 3)
+    colors_flat = (colors.reshape(-1, 3) * 255).astype(np.uint8)
+    conf = conf.reshape(-1)
+
+    cam_to_world_mat = closed_form_inverse_se3(extrinsics_cam)  # shape (S, 4, 4) typically
+    # For convenience, we store only (3,4) portion
+    cam_to_world = cam_to_world_mat[:, :3, :]
+
+    # Compute scene center and recenter
+    scene_center = np.mean(points, axis=0)
+    points_centered = points - scene_center
+    cam_to_world[..., -1] -= scene_center
+
+    # Store frame indices so we can filter by frame
+    frame_indices = np.repeat(np.arange(S), H * W)
+
+    # Build the viser GUI
+    gui_show_frames = server.gui.add_checkbox(
+        "Show Cameras",
+        initial_value=True,
+    )
+
+    # Now the slider represents percentage of points to filter out
+    gui_points_conf = server.gui.add_slider(
+        "Confidence Percent",
+        min=0,
+        max=100,
+        step=0.1,
+        initial_value=init_conf_threshold,
+    )
+
+    gui_frame_selector = server.gui.add_dropdown(
+        "Show Points from Frames",
+        options=["All"] + [str(i) for i in range(S)],
+        initial_value="All",
+    )
+
+    # Create the main point cloud handle
+    # Compute the threshold value as the given percentile
+    init_threshold_val = np.percentile(conf, init_conf_threshold)
+    init_conf_mask = conf > init_threshold_val
+    point_cloud = server.scene.add_point_cloud(
+        name="viser_pcd",
+        points=points_centered[init_conf_mask],
+        colors=colors_flat[init_conf_mask],
+        # point_size=0.0001,
+        point_size=0.001,
+        point_shape="circle",
+    )
+
+    # We will store references to frames & frustums so we can toggle visibility
+    frames: List[viser.FrameHandle] = []
+    frustums: List[viser.CameraFrustumHandle] = []
+
+    def visualize_frames(extrinsics: np.ndarray, images_: np.ndarray) -> None:
+        """
+        Add camera frames and frustums to the scene.
+        extrinsics: (S, 3, 4)
+        images_:    (S, 3, H, W)
+        """
+        # Clear any existing frames or frustums
+        for f in frames:
+            f.remove()
+        frames.clear()
+        for fr in frustums:
+            fr.remove()
+        frustums.clear()
+
+        # Optionally attach a callback that sets the viewpoint to the chosen camera
+        def attach_callback(frustum: viser.CameraFrustumHandle, frame: viser.FrameHandle) -> None:
+            @frustum.on_click
+            def _(_) -> None:
+                for client in server.get_clients().values():
+                    client.camera.wxyz = frame.wxyz
+                    client.camera.position = frame.position
+
+        img_ids = range(S)
+        for img_id in tqdm(img_ids):
+            cam2world_3x4 = extrinsics[img_id]
+            T_world_camera = viser_tf.SE3.from_matrix(cam2world_3x4)
+
+            # Add a small frame axis
+            frame_axis = server.scene.add_frame(
+                f"frame_{img_id}",
+                wxyz=T_world_camera.rotation().wxyz,
+                position=T_world_camera.translation(),
+                axes_length=0.05,
+                axes_radius=0.002,
+                origin_radius=0.002,
+            )
+            frames.append(frame_axis)
+
+            # Convert the image for the frustum
+            img = images_[img_id]  # shape (3, H, W)
+            img = (img.transpose(1, 2, 0) * 255).astype(np.uint8)
+            h, w = img.shape[:2]
+
+            # If you want correct FOV from intrinsics, do something like:
+            # fx = intrinsics_cam[img_id, 0, 0]
+            # fov = 2 * np.arctan2(h/2, fx)
+            # For demonstration, we pick a simple approximate FOV:
+            fy = 1.1 * h
+            fov = 2 * np.arctan2(h / 2, fy)
+
+            # Add the frustum
+            frustum_cam = server.scene.add_camera_frustum(
+                f"frame_{img_id}/frustum",
+                fov=fov,
+                aspect=w / h,
+                scale=0.05,
+                image=img,
+                line_width=1.0,
+            )
+            frustums.append(frustum_cam)
+            attach_callback(frustum_cam, frame_axis)
+
+    def update_point_cloud() -> None:
+        """Update the point cloud based on current GUI selections."""
+        # Here we compute the threshold value based on the current percentage
+        current_percentage = gui_points_conf.value
+        threshold_val = np.percentile(conf, current_percentage)
+        conf_mask = conf > threshold_val
+
+        if gui_frame_selector.value == "All":
+            frame_mask = np.ones_like(conf_mask, dtype=bool)
+        else:
+            selected_idx = int(gui_frame_selector.value)
+            frame_mask = frame_indices == selected_idx
+
+        combined_mask = conf_mask & frame_mask
+        point_cloud.points = points_centered[combined_mask]
+        point_cloud.colors = colors_flat[combined_mask]
+
+    @gui_points_conf.on_update
+    def _(_) -> None:
+        update_point_cloud()
+
+    @gui_frame_selector.on_update
+    def _(_) -> None:
+        update_point_cloud()
+
+    @gui_show_frames.on_update
+    def _(_) -> None:
+        """Toggle visibility of camera frames and frustums."""
+        for f in frames:
+            f.visible = gui_show_frames.value
+        for fr in frustums:
+            fr.visible = gui_show_frames.value
+
+    # Add the camera frames to the scene
+    visualize_frames(cam_to_world, images)
+
+    print("Starting viser server...")
+    # If background_mode is True, spawn a daemon thread so the main thread can continue.
+    if background_mode:
+
+        def server_loop():
+            while True:
+                time.sleep(0.001)
+
+        thread = threading.Thread(target=server_loop, daemon=True)
+        thread.start()
+    else:
+        while True:
+            time.sleep(0.01)
+
+    return server
+
+
+# Helper functions for sky segmentation
+
+def download_file_from_url(url, filename):
+    """Downloads a file from a Hugging Face model repo, handling redirects."""
+    try:
+        # Get the redirect URL
+        response = requests.get(url, allow_redirects=False)
+        response.raise_for_status()  # Raise HTTPError for bad requests (4xx or 5xx)
+
+        if response.status_code == 302:  # Expecting a redirect
+            redirect_url = response.headers["Location"]
+            response = requests.get(redirect_url, stream=True)
+            response.raise_for_status()
+        else:
+            print(f"Unexpected status code: {response.status_code}")
+            return
+
+        with open(filename, "wb") as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+        print(f"Downloaded {filename} successfully.")
+
+    except requests.exceptions.RequestException as e:
+        print(f"Error downloading file: {e}")
+
+
+
+def apply_sky_segmentation(conf: np.ndarray, image_folder: str) -> np.ndarray:
+    """
+    Apply sky segmentation to confidence scores.
+
+    Args:
+        conf (np.ndarray): Confidence scores with shape (S, H, W)
+        image_folder (str): Path to the folder containing input images
+
+    Returns:
+        np.ndarray: Updated confidence scores with sky regions masked out
+    """
+    S, H, W = conf.shape
+    sky_masks_dir = image_folder.rstrip('/') + "_sky_masks"
+    os.makedirs(sky_masks_dir, exist_ok=True)
+    
+    # Download skyseg.onnx if it doesn't exist
+    if not os.path.exists("skyseg.onnx"):
+        print("Downloading skyseg.onnx...")
+        download_file_from_url(
+            "https://huggingface.co/JianyuanWang/skyseg/resolve/main/skyseg.onnx", "skyseg.onnx"
+        )
+    
+    skyseg_session = onnxruntime.InferenceSession("skyseg.onnx")        
+    image_files = sorted(glob.glob(os.path.join(image_folder, "*")))
+    sky_mask_list = []
+    
+    print("Generating sky masks...")
+    for i, image_path in enumerate(tqdm(image_files[:S])):  # Limit to the number of images in the batch
+        image_name = os.path.basename(image_path)
+        mask_filepath = os.path.join(sky_masks_dir, image_name)
+        
+        if os.path.exists(mask_filepath):
+            sky_mask = cv2.imread(mask_filepath, cv2.IMREAD_GRAYSCALE)
+        else:
+            sky_mask = segment_sky(image_path, skyseg_session, mask_filepath)
+        
+        # Resize mask to match H×W if needed
+        if sky_mask.shape[0] != H or sky_mask.shape[1] != W:
+            sky_mask = cv2.resize(sky_mask, (W, H))
+        
+        sky_mask_list.append(sky_mask)
+        
+        
+    # Convert list to numpy array with shape S×H×W
+    sky_mask_array = np.array(sky_mask_list)
+    # Apply sky mask to confidence scores
+    sky_mask_binary = (sky_mask_array > 0.01).astype(np.float32)
+    conf = conf * sky_mask_binary
+    
+    print("Sky segmentation applied successfully")
+    return conf
+
+
+
+def segment_sky(image_path, onnx_session, mask_filename=None):
+    """
+    Segments sky from an image using an ONNX model.
+
+    Args:
+        image_path: Path to input image
+        onnx_session: ONNX runtime session with loaded model
+        mask_filename: Path to save the output mask
+
+    Returns:
+        np.ndarray: Binary mask where 255 indicates non-sky regions
+    """
+    assert mask_filename is not None
+    image = cv2.imread(image_path)
+
+    result_map = run_skyseg(onnx_session, [320, 320], image)
+    # resize the result_map to the original image size
+    result_map_original = cv2.resize(result_map, (image.shape[1], image.shape[0]))
+
+    output_mask = np.zeros_like(result_map_original)
+    output_mask[result_map_original < 1] = 1
+    output_mask = output_mask.astype(np.uint8) * 255
+    os.makedirs(os.path.dirname(mask_filename), exist_ok=True)
+    cv2.imwrite(mask_filename, output_mask)
+    return output_mask
+
+
+def run_skyseg(onnx_session, input_size, image):
+    """
+    Runs sky segmentation inference using ONNX model.
+
+    Args:
+        onnx_session: ONNX runtime session
+        input_size: Target size for model input (width, height)
+        image: Input image in BGR format
+
+    Returns:
+        np.ndarray: Segmentation mask
+    """
+    # Pre process:Resize, BGR->RGB, Transpose, PyTorch standardization, float32 cast
+    temp_image = copy.deepcopy(image)
+    resize_image = cv2.resize(temp_image, dsize=(input_size[0], input_size[1]))
+    x = cv2.cvtColor(resize_image, cv2.COLOR_BGR2RGB)
+    x = np.array(x, dtype=np.float32)
+    mean = [0.485, 0.456, 0.406]
+    std = [0.229, 0.224, 0.225]
+    x = (x / 255 - mean) / std
+    x = x.transpose(2, 0, 1)
+    x = x.reshape(-1, 3, input_size[0], input_size[1]).astype("float32")
+
+    # Inference
+    input_name = onnx_session.get_inputs()[0].name
+    output_name = onnx_session.get_outputs()[0].name
+    onnx_result = onnx_session.run([output_name], {input_name: x})
+
+    # Post process
+    onnx_result = np.array(onnx_result).squeeze()
+    min_value = np.min(onnx_result)
+    max_value = np.max(onnx_result)
+    onnx_result = (onnx_result - min_value) / (max_value - min_value)
+    onnx_result *= 255
+    onnx_result = onnx_result.astype("uint8")
+
+    return onnx_result
+
+
+
+
+
+
+parser = argparse.ArgumentParser(description="VGGT demo with viser for 3D visualization")
+parser.add_argument(
+    "--image_folder", type=str, default="examples/kitchen/images/", help="Path to folder containing images"
+)
+parser.add_argument("--use_point_map", action="store_true", help="Use point map instead of depth-based points")
+parser.add_argument("--background_mode", action="store_true", help="Run the viser server in background mode")
+parser.add_argument("--port", type=int, default=8080, help="Port number for the viser server")
+parser.add_argument(
+    "--conf_threshold", type=float, default=25.0, help="Initial percentage of low-confidence points to filter out"
+)
+parser.add_argument("--mask_sky", action="store_true", help="Apply sky segmentation to filter out sky points")
+
+
+def main():
+    """
+    Main function for the VGGT demo with viser for 3D visualization.
+    
+    This function:
+    1. Loads the VGGT model
+    2. Processes input images from the specified folder
+    3. Runs inference to generate 3D points and camera poses
+    4. Optionally applies sky segmentation to filter out sky points
+    5. Visualizes the results using viser
+    
+    Command-line arguments:
+    --image_folder: Path to folder containing input images
+    --use_point_map: Use point map instead of depth-based points
+    --background_mode: Run the viser server in background mode
+    --port: Port number for the viser server
+    --conf_threshold: Initial percentage of low-confidence points to filter out
+    --mask_sky: Apply sky segmentation to filter out sky points
+    """
+    args = parser.parse_args()
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Using device: {device}")
+
+    print("Initializing and loading VGGT model...")
+    model = VGGT()
+    _URL = "https://huggingface.co/facebook/VGGT-1B/resolve/main/model.pt"
+    model.load_state_dict(torch.hub.load_state_dict_from_url(_URL))
+
+    model.eval()
+    model = model.to(device)
+
+    # Use the provided image folder path
+    print(f"Loading images from {args.image_folder}...")
+    image_names = glob.glob(os.path.join(args.image_folder, "*"))
+    print(f"Found {len(image_names)} images")
+
+    images = load_and_preprocess_images(image_names).to(device)
+    print(f"Preprocessed images shape: {images.shape}")
+
+    print("Running inference...")
+    with torch.no_grad():
+        with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+            predictions = model(images)
+
+    print("Converting pose encoding to extrinsic and intrinsic matrices...")
+    extrinsic, intrinsic = pose_encoding_to_extri_intri(predictions["pose_enc"], images.shape[-2:])
+    predictions["extrinsic"] = extrinsic
+    predictions["intrinsic"] = intrinsic
+
+    print("Processing model outputs...")
+    for key in predictions.keys():
+        if isinstance(predictions[key], torch.Tensor):
+            predictions[key] = predictions[key].cpu().numpy().squeeze(0)  # remove batch dimension and convert to numpy
+
+    if args.use_point_map:
+        print("Visualizing 3D points from point map")
+    else:
+        print("Visualizing 3D points by unprojecting depth map by cameras")
+
+    if args.mask_sky:
+        print("Sky segmentation enabled - will filter out sky points")
+
+    print("Starting viser visualization...")
+
+    viser_server = viser_wrapper(
+        predictions,
+        port=args.port,
+        init_conf_threshold=args.conf_threshold,
+        use_point_map=args.use_point_map,
+        background_mode=args.background_mode,
+        mask_sky=args.mask_sky,
+        image_folder=args.image_folder,
+    )
+    print("Visualization complete")
+
+
+if __name__ == "__main__":
+    main()