base on DUSt3R: Geometric 3D Vision Made Easy  Official implementation of `DUSt3R: Geometric 3D Vision Made Easy` [[Project page](https://dust3r.europe.naverlabs.com/)], [[DUSt3R arxiv](https://arxiv.org/abs/2312.14132)] > **Make sure to also check [MASt3R](https://github.com/naver/mast3r): Our new model with a local feature head, metric pointmaps, and a more scalable global alignment!**   ```bibtex @inproceedings{dust3r_cvpr24, title={DUSt3R: Geometric 3D Vision Made Easy}, author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud}, booktitle = {CVPR}, year = {2024} } @misc{dust3r_arxiv23, 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 - [Table of Contents](#table-of-contents) - [License](#license) - [Get Started](#get-started) - [Installation](#installation) - [Checkpoints](#checkpoints) - [Interactive demo](#interactive-demo) - [Interactive demo with docker](#interactive-demo-with-docker) - [Usage](#usage) - [Training](#training) - [Datasets](#datasets) - [Demo](#demo) - [Our Hyperparameters](#our-hyperparameters) ## License The code is distributed under the CC BY-NC-SA 4.0 License. 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 # Optional: you can also install additional packages to: # - add support for HEIC images # - add pyrender, used to render depthmap in some datasets preprocessing # - add required packages for visloc.py pip install -r requirements_optional.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 ../../../ ``` ### Checkpoints You can obtain the checkpoints by two ways: 1) You can use our huggingface_hub integration: the models will be downloaded automatically. 2) Otherwise, 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) To download a specific model, for example `DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth`: ```bash mkdir -p checkpoints/ wget https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth -P checkpoints/ ``` For the checkpoints, make sure to agree to the license of all the public training datasets and base checkpoints we used, in addition to CC-BY-NC-SA 4.0. Again, see [section: Our Hyperparameters](#our-hyperparameters) for details. ### 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. ```bash python3 demo.py --model_name DUSt3R_ViTLarge_BaseDecoder_512_dpt # Use --weights to load a checkpoint from a local file, eg --weights checkpoints/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth # Use --image_size to select the correct resolution for the selected 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" ``` ### Interactive demo with docker To run DUSt3R using Docker, including with NVIDIA CUDA support, follow these instructions: 1. **Install Docker**: If not already installed, download and install `docker` and `docker compose` from the [Docker website](https://www.docker.com/get-started). 2. **Install NVIDIA Docker Toolkit**: For GPU support, install the NVIDIA Docker toolkit from the [Nvidia website](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html). 3. **Build the Docker image and run it**: `cd` into the `./docker` directory and run the following commands: ```bash cd docker bash run.sh --with-cuda --model_name="DUSt3R_ViTLarge_BaseDecoder_512_dpt" ``` Or if you want to run the demo without CUDA support, run the following command: ```bash cd docker bash run.sh --model_name="DUSt3R_ViTLarge_BaseDecoder_512_dpt" ``` By default, `demo.py` is lanched with the option `--local_network`. Visit `http://localhost:7860/` to access the web UI (or replace `localhost` with the machine's name to access it from the network). `run.sh` will launch docker-compose using either the [docker-compose-cuda.yml](docker/docker-compose-cuda.yml) or [docker-compose-cpu.ym](docker/docker-compose-cpu.yml) config file, then it starts the demo using [entrypoint.sh](docker/files/entrypoint.sh).  ## Usage ```python from dust3r.inference import inference from dust3r.model import AsymmetricCroCo3DStereo 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__': device = 'cuda' batch_size = 1 schedule = 'cosine' lr = 0.01 niter = 300 model_name = "naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt" # you can put the path to a local checkpoint in model_name if needed model = AsymmetricCroCo3DStereo.from_pretrained(model_name).to(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 a short demonstration to get started with training DUSt3R. ### Datasets At this moment, we have added the following training datasets: - [CO3Dv2](https://github.com/facebookresearch/co3d) - [Creative Commons Attribution-NonCommercial 4.0 International](https://github.com/facebookresearch/co3d/blob/main/LICENSE) - [ARKitScenes](https://github.com/apple/ARKitScenes) - [Creative Commons Attribution-NonCommercial-ShareAlike 4.0](https://github.com/apple/ARKitScenes/tree/main?tab=readme-ov-file#license) - [ScanNet++](https://kaldir.vc.in.tum.de/scannetpp/) - [non-commercial research and educational purposes](https://kaldir.vc.in.tum.de/scannetpp/static/scannetpp-terms-of-use.pdf) - [BlendedMVS](https://github.com/YoYo000/BlendedMVS) - [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/) - [WayMo Open dataset](https://github.com/waymo-research/waymo-open-dataset) - [Non-Commercial Use](https://waymo.com/open/terms/) - [Habitat-Sim](https://github.com/facebookresearch/habitat-sim/blob/main/DATASETS.md) - [MegaDepth](https://www.cs.cornell.edu/projects/megadepth/) - [StaticThings3D](https://github.com/lmb-freiburg/robustmvd/blob/master/rmvd/data/README.md#staticthings3d) - [WildRGB-D](https://github.com/wildrgbd/wildrgbd/) For each dataset, we provide a preprocessing script in the `datasets_preprocess` directory and an archive containing the list of pairs when needed. You have to download the datasets yourself from their official sources, agree to their license, download our list of pairs, and run the preprocessing script. Links: [ARKitScenes pairs](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/arkitscenes_pairs.zip) [ScanNet++ pairs](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/scannetpp_pairs.zip) [BlendedMVS pairs](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/blendedmvs_pairs.npy) [WayMo Open dataset pairs](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/waymo_pairs.npz) [Habitat metadata](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/habitat_5views_v1_512x512_metadata.tar.gz) [MegaDepth pairs](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/megadepth_pairs.npz) [StaticThings3D pairs](https://download.europe.naverlabs.com/ComputerVision/DUSt3R/staticthings_pairs.npy) > [!NOTE] > They are not strictly equivalent to what was used to train DUSt3R, but they should be close enough. ### Demo For this training demo, 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. ```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 --disable_cudnn_benchmark \ --output_dir "checkpoints/dust3r_demo_512dpt" ``` ### Our Hyperparameters Here are the commands we used for training the models: ```bash # NOTE: ROOT path omitted for datasets # 224 linear torchrun --nproc_per_node 8 train.py \ --train_dataset=" + 100_000 @ Habitat(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 @ MegaDepth(split='train', aug_crop=16, resolution=224, transform=ColorJitter) + 100_000 @ ARKitScenes(aug_crop=256, resolution=224, transform=ColorJitter) + 100_000 @ Co3d(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 @ InternalUnreleasedDataset(aug_crop=128, resolution=224, transform=ColorJitter) " \ --test_dataset=" Habitat(1_000, split='val', resolution=224, seed=777) + 1_000 @ BlendedMVS(split='val', resolution=224, seed=777) + 1_000 @ MegaDepth(split='val', resolution=224, seed=777) + 1_000 @ Co3d(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 @ Habitat(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 @ MegaDepth(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(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 @ InternalUnreleasedDataset(aug_crop=128, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) " \ --test_dataset=" Habitat(1_000, split='val', resolution=(512,384), seed=777) + 1_000 @ BlendedMVS(split='val', resolution=(512,384), seed=777) + 1_000 @ MegaDepth(split='val', resolution=(512,336), seed=777) + 1_000 @ Co3d(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=100 --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 @ Habitat(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 @ MegaDepth(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(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 @ InternalUnreleasedDataset(aug_crop=128, resolution=[(512, 384), (512, 336), (512, 288), (512, 256), (512, 160)], transform=ColorJitter) " \ --test_dataset=" Habitat(1_000, split='val', resolution=(512,384), seed=777) + 1_000 @ BlendedMVS(split='val', resolution=(512,384), seed=777) + 1_000 @ MegaDepth(split='val', resolution=(512,336), seed=777) + 1_000 @ Co3d(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=4 --accum_iter=2 \ --save_freq=5 --keep_freq=10 --eval_freq=1 --print_freq=10 --disable_cudnn_benchmark \ --output_dir="checkpoints/dust3r_512dpt" ``` ", Assign "at most 3 tags" to the expected json: {"id":"8238","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"