Add comprehensive dataset card for Multi-View 3D Point Tracking datasets

README.md

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+---
+task_categories:
+  - keypoint-detection
+tags:
+  - 3d-tracking
+  - multi-view
+  - point-cloud
+  - computer-vision
+  - robotics
+  - synthetic-data
+  - real-world-data
+  - pytorch
+  - pytorch-hub
+language: en
+---
+
+# Multi-View 3D Point Tracking Datasets
+
+This repository hosts the training and evaluation datasets associated with the paper [**Multi-View 3D Point Tracking**](https://huggingface.co/papers/2508.21060).
+
+**Project Page:** [https://ethz-vlg.github.io/mvtracker/](https://ethz-vlg.github.io/mvtracker/)
+**Code/Github Repository:** [https://github.com/ethz-vlg/mvtracker](https://github.com/ethz-vlg/mvtracker)
+
+## Abstract
+
+We introduce the first data-driven multi-view 3D point tracker, designed to track arbitrary points in dynamic scenes using multiple camera views. Unlike existing monocular trackers, which struggle with depth ambiguities and occlusion, or prior multi-camera methods that require over 20 cameras and tedious per-sequence optimization, our feed-forward model directly predicts 3D correspondences using a practical number of cameras (e.g., four), enabling robust and accurate online tracking. Given known camera poses and either sensor-based or estimated multi-view depth, our tracker fuses multi-view features into a unified point cloud and applies k-nearest-neighbors correlation alongside a transformer-based update to reliably estimate long-range 3D correspondences, even under occlusion. We train on 5K synthetic multi-view Kubric sequences and evaluate on two real-world benchmarks: Panoptic Studio and DexYCB, achieving median trajectory errors of 3.1 cm and 2.0 cm, respectively. Our method generalizes well to diverse camera setups of 1-8 views with varying vantage points and video lengths of 24-150 frames. By releasing our tracker alongside training and evaluation datasets, we aim to set a new standard for multi-view 3D tracking research and provide a practical tool for real-world applications.
+
+## Dataset Details
+
+To benchmark multi-view 3D point tracking, we provide preprocessed versions of three datasets:
+
+-   **MV-Kubric**: a synthetic training dataset adapted from single-view Kubric into a multi-view setting.
+-   **Panoptic Studio**: evaluation benchmark with real-world activities such as basketball, juggling, and toy play (10 sequences).
+-   **DexYCB**: evaluation benchmark with real-world hand–object interactions (10 sequences).
+
+You can download and extract them as (~72 GB after extraction):
+
+```bash
+# MV-Kubric (simulated + DUSt3R depths)
+wget https://huggingface.co/datasets/ethz-vlg/mv3dpt-datasets/resolve/main/kubric-multiview--test.tar.gz -P datasets/
+wget https://huggingface.co/datasets/ethz-vlg/mv3dpt-datasets/resolve/main/kubric-multiview--test--dust3r-depth.tar.gz -P datasets/
+tar -xvzf datasets/kubric-multiview--test.tar.gz -C datasets/
+tar -xvzf datasets/kubric-multiview--test--dust3r-depth.tar.gz -C datasets/
+rm datasets/kubric-multiview*.tar.gz
+
+# Panoptic Studio (optimization-based depth from Dynamic3DGS)
+wget https://huggingface.co/datasets/ethz-vlg/mv3dpt-datasets/resolve/main/panoptic-multiview.tar.gz -P datasets/
+tar -xvzf datasets/panoptic-multiview.tar.gz -C datasets/
+rm datasets/panoptic-multiview.tar.gz
+
+# DexYCB (Kinect + DUSt3R depths)
+wget https://huggingface.co/datasets/ethz-vlg/mv3dpt-datasets/resolve/main/dex-ycb-multiview.tar.gz -P datasets/
+wget https://huggingface.co/datasets/ethz-vlg/mv3dpt-datasets/resolve/main/dex-ycb-multiview--dust3r-depth.tar.gz -P datasets/
+tar -xvzf datasets/dex-ycb-multiview.tar.gz -C datasets/
+tar -xvzf datasets/dex-ycb-multiview--dust3r-depth.tar.gz -C datasets/
+rm datasets/dex-ycb-multiview*.tar.gz
+```
+
+For licensing and usage terms, please refer to the original datasets from which these preprocessed versions are derived.
+
+## Sample Usage
+
+This dataset repository contains the data for the MVTracker model. With minimal dependencies in place (as described in the [GitHub repository](https://github.com/ethz-vlg/mvtracker#quick-start)), you can try MVTracker directly via **PyTorch Hub**:
+
+```python
+import torch
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+mvtracker = torch.hub.load("ethz-vlg/mvtracker", "mvtracker", pretrained=True, device=device)
+
+# Example input from demo sample (downloaded automatically)
+sample = np.load(hf_hub_download("ethz-vlg/mvtracker", "data_sample.npz"))
+rgbs = torch.from_numpy(sample["rgbs"]).float()
+depths = torch.from_numpy(sample["depths"]).float()
+intrs = torch.from_numpy(sample["intrs"]).float()
+extrs = torch.from_numpy(sample["extrs"]).float()
+query_points = torch.from_numpy(sample["query_points"]).float()
+
+with torch.no_grad():
+    results = mvtracker(
+        rgbs=rgbs[None].to(device) / 255.0,
+        depths=depths[None].to(device),
+        intrs=intrs[None].to(device),
+        extrs=extrs[None].to(device),
+        query_points_3d=query_points[None].to(device),
+    )
+
+pred_tracks = results["traj_e"].cpu()  # [T,N,3]
+pred_vis = results["vis_e"].cpu()      # [T,N]
+print(pred_tracks.shape, pred_vis.shape)
+```
+
+## Citation
+
+If you find our repository useful, please consider giving it a star ⭐ and citing our work:
+
+```bibtex
+@inproceedings{rajic2025mvtracker,
+  title     = {Multi-View 3D Point Tracking},
+  author    = {Raji{\v{c}}, Frano and Xu, Haofei and Mihajlovic, Marko and Li, Siyuan and Demir, Irem and G{\"u}ndo{\u{g}}du, Emircan and Ke, Lei and Prokudin, Sergey and Pollefeys, Marc and Tang, Siyu},
+  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+  year      = {2025}
+}
+```