DUSt3R

Official implementation of DUSt3R: Geometric 3D Vision Made Easy
[Project page], [DUSt3R arxiv]

@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}
}

DUSt3R

License

The code is distributed under the CC BY-NC-SA 4.0 License. See LICENSE for more information.

# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).

Get Started

Installation

Clone DUSt3R

git clone --recursive https://github.com/naver/dust3r
cd dust3r
# if you have already cloned dust3r:
# git submodule update --init --recursive

Create the environment, here we show an example using conda.

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

Optional, compile the cuda kernels for RoPE (as in CroCo v2)

# 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 ../../../

Download pre-trained model

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`	224x224	Linear	ViT-L	ViT-B
`DUSt3R_ViTLarge_BaseDecoder_512_linear.pth`	512x384, 512x336, 512x288, 512x256, 512x160	Linear	ViT-L	ViT-B
`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

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 adjust 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.

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

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 - Creative Commons Attribution-NonCommercial 4.0 International 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

# 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:

# 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'

MisterEkole/dust3r