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third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/README.md

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----
-tags:
-- vision
----
-
-## DUSt3R
-
-# Model info
-
-Project page: https://dust3r.europe.naverlabs.com/
-
-# How to use
-
-Here's how to load the model (after [installing](https://github.com/naver/dust3r?tab=readme-ov-file#installation) the dust3r package):
-
-```python
-from dust3r.model import AsymmetricCroCo3DStereo
-import torch
-
-model = AsymmetricCroCo3DStereo.from_pretrained("nielsr/DUSt3R_ViTLarge_BaseDecoder_512_dpt")
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-```
-
-Next, one can run inference as follows:
-
-```
-from dust3r.inference import inference
-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__':
-    batch_size = 1
-    schedule = 'cosine'
-    lr = 0.01
-    niter = 300
-
-    # 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)
-
-```
-
-### BibTeX entry and citation info
-
-```bibtex
-@journal{dust3r2023,
-title={{DUSt3R: Geometric 3D Vision Made Easy}}, 
-author={{Wang, Shuzhe and Leroy, Vincent and Cabon, Yohann and Chidlovskii, Boris and Revaud Jerome}}, 
-journal={arXiv preprint 2312.14132},
-year={2023}}
-```

third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/config.json

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-{
-  "output_mode": "pts3d",
-  "head_type": "dpt",
-  "depth_mode": [
-    "exp",
-    -Infinity,
-    Infinity
-  ],
-  "conf_mode": [
-    "exp",
-    1,
-    Infinity
-  ],
-  "freeze": "none",
-  "landscape_only": false,
-  "patch_embed_cls": "PatchEmbedDust3R",
-  "enc_depth": 24,
-  "dec_depth": 12,
-  "enc_embed_dim": 1024,
-  "dec_embed_dim": 768,
-  "enc_num_heads": 16,
-  "dec_num_heads": 12,
-  "pos_embed": "RoPE100",
-  "img_size": [
-    512,
-    512
-  ]
-}

third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/model.safetensors

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