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# DUSt3R
Official implementation of `DUSt3R: Geometric 3D Vision Made Easy`
[[Project page](https://dust3r.europe.naverlabs.com/)], [[DUSt3R arxiv](https://arxiv.org/abs/2312.14132)]
```bibtex
@misc{wang2023dust3r,
title={DUSt3R: Geometric 3D Vision Made Easy},
author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud},
year={2023},
eprint={2312.14132},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Table of Contents
- [DUSt3R](#dust3r)
- [License](#license)
- [Get Started](#get-started)
- [Installation](#installation)
- [Checkpoints](#checkpoints)
- [Interactive demo](#interactive-demo)
- [Usage](#usage)
- [Training](#training)
- [Demo](#demo)
- [Our Hyperparameters](#our-hyperparameters)
## License
The code is distributed under the CC BY-NC-SA 4.0 License. See See [LICENSE](LICENSE) for more information.
```python
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
```
## Get Started
### Installation
1. Clone DUSt3R
```bash
git clone --recursive https://github.com/naver/dust3r
cd dust3r
# if you have already cloned dust3r:
# git submodule update --init --recursive
```
2. Create the environment, here we show an example using conda.
```bash
conda create -n dust3r python=3.11 cmake=3.14.0
conda activate dust3r
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia # use the correct version of cuda for your system
pip install -r requirements.txt
```
3. Optional, compile the cuda kernels for RoPE (as in CroCo v2)
```bash
# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.
cd croco/models/curope/
python setup.py build_ext --inplace
cd ../../../
```
4. Download pre-trained model
```bash
mkdir -p checkpoints/
wget https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth -P checkpoints/
```
### Checkpoints
We provide several pre-trained models:
| Modelname | Training resolutions | Head | Encoder | Decoder |
|-------------|----------------------|------|---------|---------|
| [`DUSt3R_ViTLarge_BaseDecoder_224_linear.pth`](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_224_linear.pth) | 224x224 | Linear | ViT-L | ViT-B |
| [`DUSt3R_ViTLarge_BaseDecoder_512_linear.pth`](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_linear.pth) | 512x384, 512x336, 512x288, 512x256, 512x160 | Linear | ViT-L | ViT-B |
| [`DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth`](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth) | 512x384, 512x336, 512x288, 512x256, 512x160 | DPT | ViT-L | ViT-B |
You can check the hyperparameters we used to train these models in the [section: Our Hyperparameters](#our-hyperparameters)
### Interactive demo
In this demo, you should be able run DUSt3R on your machine to reconstruct a scene.
First select images that depicts the same scene.
You can ajust the global alignment schedule and its number of iterations.
Note: if you selected one or two images, the global alignment procedure will be skipped (mode=GlobalAlignerMode.PairViewer)
Hit "Run" and wait.
When the global alignment ends, the reconstruction appears.
Use the slider "min_conf_thr" to show or remove low confidence areas.
```bash
python3 demo.py --weights checkpoints/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth
# Use --image_size to select the correct resolution for your checkpoint. 512 (default) or 224
# Use --local_network to make it accessible on the local network, or --server_name to specify the url manually
# Use --server_port to change the port, by default it will search for an available port starting at 7860
# Use --device to use a different device, by default it's "cuda"
```
## Usage
```python
from dust3r.inference import inference, load_model
from dust3r.utils.image import load_images
from dust3r.image_pairs import make_pairs
from dust3r.cloud_opt import global_aligner, GlobalAlignerMode
if __name__ == '__main__':
model_path = "checkpoints/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth"
device = 'cuda'
batch_size = 1
schedule = 'cosine'
lr = 0.01
niter = 300
model = load_model(model_path, device)
# load_images can take a list of images or a directory
images = load_images(['croco/assets/Chateau1.png', 'croco/assets/Chateau2.png'], size=512)
pairs = make_pairs(images, scene_graph='complete', prefilter=None, symmetrize=True)
output = inference(pairs, model, device, batch_size=batch_size)
# at this stage, you have the raw dust3r predictions
view1, pred1 = output['view1'], output['pred1']
view2, pred2 = output['view2'], output['pred2']
# here, view1, pred1, view2, pred2 are dicts of lists of len(2)
# -> because we symmetrize we have (im1, im2) and (im2, im1) pairs
# in each view you have:
# an integer image identifier: view1['idx'] and view2['idx']
# the img: view1['img'] and view2['img']
# the image shape: view1['true_shape'] and view2['true_shape']
# an instance string output by the dataloader: view1['instance'] and view2['instance']
# pred1 and pred2 contains the confidence values: pred1['conf'] and pred2['conf']
# pred1 contains 3D points for view1['img'] in view1['img'] space: pred1['pts3d']
# pred2 contains 3D points for view2['img'] in view1['img'] space: pred2['pts3d_in_other_view']
# next we'll use the global_aligner to align the predictions
# depending on your task, you may be fine with the raw output and not need it
# with only two input images, you could use GlobalAlignerMode.PairViewer: it would just convert the output
# if using GlobalAlignerMode.PairViewer, no need to run compute_global_alignment
scene = global_aligner(output, device=device, mode=GlobalAlignerMode.PointCloudOptimizer)
loss = scene.compute_global_alignment(init="mst", niter=niter, schedule=schedule, lr=lr)
# retrieve useful values from scene:
imgs = scene.imgs
focals = scene.get_focals()
poses = scene.get_im_poses()
pts3d = scene.get_pts3d()
confidence_masks = scene.get_masks()
# visualize reconstruction
scene.show()
# find 2D-2D matches between the two images
from dust3r.utils.geometry import find_reciprocal_matches, xy_grid
pts2d_list, pts3d_list = [], []
for i in range(2):
conf_i = confidence_masks[i].cpu().numpy()
pts2d_list.append(xy_grid(*imgs[i].shape[:2][::-1])[conf_i]) # imgs[i].shape[:2] = (H, W)
pts3d_list.append(pts3d[i].detach().cpu().numpy()[conf_i])
reciprocal_in_P2, nn2_in_P1, num_matches = find_reciprocal_matches(*pts3d_list)
print(f'found {num_matches} matches')
matches_im1 = pts2d_list[1][reciprocal_in_P2]
matches_im0 = pts2d_list[0][nn2_in_P1][reciprocal_in_P2]
# visualize a few matches
import numpy as np
from matplotlib import pyplot as pl
n_viz = 10
match_idx_to_viz = np.round(np.linspace(0, num_matches-1, n_viz)).astype(int)
viz_matches_im0, viz_matches_im1 = matches_im0[match_idx_to_viz], matches_im1[match_idx_to_viz]
H0, W0, H1, W1 = *imgs[0].shape[:2], *imgs[1].shape[:2]
img0 = np.pad(imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
img1 = np.pad(imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
img = np.concatenate((img0, img1), axis=1)
pl.figure()
pl.imshow(img)
cmap = pl.get_cmap('jet')
for i in range(n_viz):
(x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T
pl.plot([x0, x1 + W0], [y0, y1], '-+', color=cmap(i / (n_viz - 1)), scalex=False, scaley=False)
pl.show(block=True)
```
## Training
In this section, we present propose a short demonstration to get started with training DUSt3R. At the moment, we didn't release the training datasets, so we're going to download and prepare a subset of [CO3Dv2](https://github.com/facebookresearch/co3d) - [Creative Commons Attribution-NonCommercial 4.0 International](https://github.com/facebookresearch/co3d/blob/main/LICENSE) and launch the training code on it.
The demo model will be trained for a few epochs on a very small dataset. It will not be very good.
### Demo
```bash
# download and prepare the co3d subset
mkdir -p data/co3d_subset
cd data/co3d_subset
git clone https://github.com/facebookresearch/co3d
cd co3d
python3 ./co3d/download_dataset.py --download_folder ../ --single_sequence_subset
rm ../*.zip
cd ../../..
python3 datasets_preprocess/preprocess_co3d.py --co3d_dir data/co3d_subset --output_dir data/co3d_subset_processed --single_sequence_subset
# download the pretrained croco v2 checkpoint
mkdir -p checkpoints/
wget https://download.europe.naverlabs.com/ComputerVision/CroCo/CroCo_V2_ViTLarge_BaseDecoder.pth -P checkpoints/
# the training of dust3r is done in 3 steps.
# for this example we'll do fewer epochs, for the actual hyperparameters we used in the paper, see the next section: "Our Hyperparameters"
# step 1 - train dust3r for 224 resolution
torchrun --nproc_per_node=4 train.py \
--train_dataset "1000 @ Co3d(split='train', ROOT='data/co3d_subset_processed', aug_crop=16, mask_bg='rand', resolution=224, transform=ColorJitter)" \
--test_dataset "100 @ Co3d(split='test', ROOT='data/co3d_subset_processed', resolution=224, seed=777)" \
--model "AsymmetricCroCo3DStereo(pos_embed='RoPE100', img_size=(224, 224), head_type='linear', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
--train_criterion "ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
--test_criterion "Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
--pretrained checkpoints/CroCo_V2_ViTLarge_BaseDecoder.pth \
--lr 0.0001 --min_lr 1e-06 --warmup_epochs 1 --epochs 10 --batch_size 16 --accum_iter 1 \
--save_freq 1 --keep_freq 5 --eval_freq 1 \
--output_dir checkpoints/dust3r_demo_224
# step 2 - train dust3r for 512 resolution
torchrun --nproc_per_node=4 train.py \
--train_dataset "1000 @ Co3d(split='train', ROOT='data/co3d_subset_processed', aug_crop=16, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter)" \
--test_dataset="100 @ Co3d(split='test', ROOT='data/co3d_subset_processed', resolution=(512,384), seed=777)" \
--model="AsymmetricCroCo3DStereo(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='linear', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
--train_criterion "ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
--test_criterion "Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
--pretrained='checkpoints/dust3r_demo_224/checkpoint-best.pth' \
--lr=0.0001 --min_lr=1e-06 --warmup_epochs 1 --epochs 10 --batch_size 4 --accum_iter 4 \
--save_freq 1 --keep_freq 5 --eval_freq 1 \
--output_dir checkpoints/dust3r_demo_512
# step 3 - train dust3r for 512 resolution with dpt
torchrun --nproc_per_node=4 train.py \
--train_dataset "1000 @ Co3d(split='train', ROOT='data/co3d_subset_processed', aug_crop=16, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter)" \
--test_dataset="100 @ Co3d(split='test', ROOT='data/co3d_subset_processed', resolution=(512,384), seed=777)" \
--model="AsymmetricCroCo3DStereo(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='dpt', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
--train_criterion "ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
--test_criterion "Regr3D_ScaleShiftInv(L21, gt_scale=True)" \
--pretrained='checkpoints/dust3r_demo_512/checkpoint-best.pth' \
--lr=0.0001 --min_lr=1e-06 --warmup_epochs 1 --epochs 10 --batch_size 2 --accum_iter 8 \
--save_freq 1 --keep_freq 5 --eval_freq 1 \
--output_dir checkpoints/dust3r_demo_512dpt
```
### Our Hyperparameters
We didn't release the training datasets, but here are the commands we used for training our models:
```bash
# NOTE: ROOT path omitted for datasets
# 224 linear
torchrun --nproc_per_node 4 train.py \
--train_dataset=" + 100_000 @ Habitat512(1_000_000, split='train', aug_crop=16, resolution=224, transform=ColorJitter) + 100_000 @ BlendedMVS(split='train', aug_crop=16, resolution=224, transform=ColorJitter) + 100_000 @ MegaDepthDense(split='train', aug_crop=16, resolution=224, transform=ColorJitter) + 100_000 @ ARKitScenes(aug_crop=256, resolution=224, transform=ColorJitter) + 100_000 @ Co3d_v3(split='train', aug_crop=16, mask_bg='rand', resolution=224, transform=ColorJitter) + 100_000 @ StaticThings3D(aug_crop=256, mask_bg='rand', resolution=224, transform=ColorJitter) + 100_000 @ ScanNetpp(split='train', aug_crop=256, resolution=224, transform=ColorJitter) + 100_000 @ Waymo(aug_crop=128, resolution=224, transform=ColorJitter) " \
--test_dataset=" Habitat512(1_000, split='val', resolution=224, seed=777) + 1_000 @ BlendedMVS(split='val', resolution=224, seed=777) + 1_000 @ MegaDepthDense(split='val', resolution=224, seed=777) + 1_000 @ Co3d_v3(split='test', mask_bg='rand', resolution=224, seed=777) " \
--train_criterion="ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
--test_criterion='Regr3D_ScaleShiftInv(L21, gt_scale=True)' \
--model="AsymmetricCroCo3DStereo(pos_embed='RoPE100', img_size=(224, 224), head_type='linear', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
--pretrained="checkpoints/CroCo_V2_ViTLarge_BaseDecoder.pth" \
--lr=0.0001 --min_lr=1e-06 --warmup_epochs=10 --epochs=100 --batch_size=16 --accum_iter=1 \
--save_freq=5 --keep_freq=10 --eval_freq=1 \
--output_dir='checkpoints/dust3r_224'
# 512 linear
torchrun --nproc_per_node 8 train.py \
--train_dataset=" + 10_000 @ Habitat512(1_000_000, split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ BlendedMVS(split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ MegaDepthDense(split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ ARKitScenes(aug_crop=256, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ Co3d_v3(split='train', aug_crop=16, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ StaticThings3D(aug_crop=256, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ ScanNetpp(split='train', aug_crop=256, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ Waymo(aug_crop=128, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) " \
--test_dataset=" Habitat512(1_000, split='val', resolution=(512,384), seed=777) + 1_000 @ BlendedMVS(split='val', resolution=(512,384), seed=777) + 1_000 @ MegaDepthDense(split='val', resolution=(512,336), seed=777) + 1_000 @ Co3d_v3(split='test', resolution=(512,384), seed=777) " \
--train_criterion="ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
--test_criterion='Regr3D_ScaleShiftInv(L21, gt_scale=True)' \
--model="AsymmetricCroCo3DStereo(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='linear', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
--pretrained='checkpoints/dust3r_224/checkpoint-best.pth' \
--lr=0.0001 --min_lr=1e-06 --warmup_epochs=20 --epochs=200 --batch_size=4 --accum_iter=2 \
--save_freq=10 --keep_freq=10 --eval_freq=1 --print_freq=10 \
--output_dir='checkpoints/dust3r_512'
# 512 dpt
torchrun --nproc_per_node 8 train.py \
--train_dataset=" + 10_000 @ Habitat512(1_000_000, split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ BlendedMVS(split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ MegaDepthDense(split='train', aug_crop=16, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ ARKitScenes(aug_crop=256, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ Co3d_v3(split='train', aug_crop=16, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ StaticThings3D(aug_crop=256, mask_bg='rand', resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ ScanNetpp(split='train', aug_crop=256, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) + 10_000 @ Waymo(aug_crop=128, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) " \
--test_dataset=" Habitat512(1_000, split='val', resolution=(512,384), seed=777) + 1_000 @ BlendedMVS(split='val', resolution=(512,384), seed=777) + 1_000 @ MegaDepthDense(split='val', resolution=(512,336), seed=777) + 1_000 @ Co3d_v3(split='test', resolution=(512,384), seed=777) " \
--train_criterion="ConfLoss(Regr3D(L21, norm_mode='avg_dis'), alpha=0.2)" \
--test_criterion='Regr3D_ScaleShiftInv(L21, gt_scale=True)' \
--model="AsymmetricCroCo3DStereo(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='dpt', output_mode='pts3d', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12)" \
--pretrained='checkpoints/dust3r_512/checkpoint-best.pth' \
--lr=0.0001 --min_lr=1e-06 --warmup_epochs=15 --epochs=90 --batch_size=2 --accum_iter=4 \
--save_freq=5 --keep_freq=10 --eval_freq=1 --print_freq=10 \
--output_dir='checkpoints/dust3r_512dpt'
```
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