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I87X: MASt3R-SLAM (base.yaml) on ScanVideos/Video2_T3; MasterSlam1 = no intrinsics, MasterSlam2 = ARCore intrinsics (--calib)

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  1. .gitattributes +2 -0
  2. I87X/MasterSlam1_ScanVideo2/config.yaml +58 -0
  3. I87X/MasterSlam1_ScanVideo2/keyframes/scan/0.0.png +3 -0
  4. I87X/MasterSlam1_ScanVideo2/keyframes/scan/11.0.png +3 -0
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  28. I87X/MasterSlam1_ScanVideo2/keyframes/scan/59.733333587646484.png +3 -0
  29. I87X/MasterSlam1_ScanVideo2/run.log +93 -0
  30. I87X/MasterSlam1_ScanVideo2/scan.ply +3 -0
  31. I87X/MasterSlam1_ScanVideo2/scan.txt +26 -0
  32. I87X/MasterSlam2_ScanVideo2/calib_arcore.yaml +4 -0
  33. I87X/MasterSlam2_ScanVideo2/config.yaml +58 -0
  34. I87X/MasterSlam2_ScanVideo2/keyframes/scan/0.0.png +3 -0
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  50. I87X/MasterSlam2_ScanVideo2/keyframes/scan/49.5.png +3 -0
.gitattributes CHANGED
@@ -1324,3 +1324,5 @@ I64/RTABMAP2/261105-111908.obj filter=lfs diff=lfs merge=lfs -text
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  P64/MasterSlam2_ScanVideo1/scan.ply filter=lfs diff=lfs merge=lfs -text
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  P64/MasterSlam1_ScanVideo1/scan.ply filter=lfs diff=lfs merge=lfs -text
 
 
 
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  I64/RTABMAP3/261105-114053-Ip14.obj filter=lfs diff=lfs merge=lfs -text
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  P64/MasterSlam2_ScanVideo1/scan.ply filter=lfs diff=lfs merge=lfs -text
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  P64/MasterSlam1_ScanVideo1/scan.ply filter=lfs diff=lfs merge=lfs -text
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+ I87X/MasterSlam1_ScanVideo2/scan.ply filter=lfs diff=lfs merge=lfs -text
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+ I87X/MasterSlam2_ScanVideo2/scan.ply filter=lfs diff=lfs merge=lfs -text
I87X/MasterSlam1_ScanVideo2/config.yaml ADDED
@@ -0,0 +1,58 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ use_calib: False
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+ single_thread: False
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+ dataset:
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+ subsample: 1
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+ img_downsample: 1
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+ center_principle_point: True
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+
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+ matching:
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+ max_iter: 10
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+ lambda_init: 1e-8
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+ convergence_thresh: 1e-6
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+ dist_thresh: 1e-1 # distance in 3D space
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+ radius: 3
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+ dilation_max: 5 # Right now starts from max and iteratively decreases until 1
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+
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+ tracking:
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+ min_match_frac: 0.05
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+ max_iters: 50
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+ C_conf: 0.0
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+ Q_conf: 1.5
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+ rel_error: 1e-3
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+ delta_norm: 1e-3
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+ huber: 1.345
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+ match_frac_thresh: 0.333
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+ sigma_ray: 0.003
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+ sigma_dist: 1e+1
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+ sigma_pixel: 1.0
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+ sigma_depth: 1e+1 # NOTE: log-depth!
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+ sigma_point: 0.05
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+ pixel_border: -10 # Only in calib (negative means allow pixels outside image up to that distance)
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+ depth_eps: 1e-6 # Only in calib case
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+ filtering_mode: weighted_pointmap # recent, first, best_score, weighted_pointmap, weighted_spherical, indep_conf
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+ filtering_score: median # median, mean (only used for filtering_mode=best_score)
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+
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+ local_opt:
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+ pin: 1
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+ window_size: 1e+6
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+ C_conf: 0.0
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+ Q_conf: 1.5
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+ min_match_frac: 0.1
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+ pixel_border: -10 # Only in calib (negative means allow pixels outside image up to that distance)
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+ depth_eps: 1e-6 # Only in calib case
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+ max_iters: 10
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+ sigma_ray: 0.003
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+ sigma_dist: 1e+1
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+ sigma_pixel: 1.0
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+ sigma_depth: 1e+1 # NOTE: log-depth!
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+ sigma_point: 0.05
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+ delta_norm: 1e-8
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+ use_cuda: True
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+
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+ retrieval:
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+ k: 3
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+ min_thresh: 5e-3
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+
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+ reloc:
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+ min_match_frac: 0.3
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+ strict: True
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I87X/MasterSlam1_ScanVideo2/run.log ADDED
@@ -0,0 +1,93 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ /home/lehoangan/LHAN/mast3r/data/I87X_T3/scan
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+ {'use_calib': False, 'single_thread': False, 'dataset': {'subsample': 1, 'img_downsample': 1, 'center_principle_point': True}, 'matching': {'max_iter': 10, 'lambda_init': 1e-08, 'convergence_thresh': 1e-06, 'dist_thresh': 0.1, 'radius': 3, 'dilation_max': 5}, 'tracking': {'min_match_frac': 0.05, 'max_iters': 50, 'C_conf': 0.0, 'Q_conf': 1.5, 'rel_error': 0.001, 'delta_norm': 0.001, 'huber': 1.345, 'match_frac_thresh': 0.333, 'sigma_ray': 0.003, 'sigma_dist': 10.0, 'sigma_pixel': 1.0, 'sigma_depth': 10.0, 'sigma_point': 0.05, 'pixel_border': -10, 'depth_eps': 1e-06, 'filtering_mode': 'weighted_pointmap', 'filtering_score': 'median'}, 'local_opt': {'pin': 1, 'window_size': 1000000.0, 'C_conf': 0.0, 'Q_conf': 1.5, 'min_match_frac': 0.1, 'pixel_border': -10, 'depth_eps': 1e-06, 'max_iters': 10, 'sigma_ray': 0.003, 'sigma_dist': 10.0, 'sigma_pixel': 1.0, 'sigma_depth': 10.0, 'sigma_point': 0.05, 'delta_norm': 1e-08, 'use_cuda': True}, 'retrieval': {'k': 3, 'min_thresh': 0.005}, 'reloc': {'min_match_frac': 0.3, 'strict': True}}
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+ ... loading model from checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth
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+ /home/lehoangan/LHAN/mast3r/MASt3R-SLAM/thirdparty/mast3r/mast3r/model.py:24: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
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+ ckpt = torch.load(model_path, map_location='cpu')
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+ instantiating : AsymmetricMASt3R(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), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), patch_embed_cls='PatchEmbedDust3R', two_confs=True, desc_conf_mode=('exp', 0, inf), landscape_only=False)
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+ <All keys matched successfully>
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+ Loading retrieval model from checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth
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+ /home/lehoangan/LHAN/mast3r/MASt3R-SLAM/thirdparty/mast3r/mast3r/retrieval/processor.py:70: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
10
+ ckpt = torch.load(modelname, 'cpu') # TODO from pretrained to download it automatically
11
+ /home/lehoangan/LHAN/mast3r/MASt3R-SLAM/mast3r_slam/frame.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /opt/conda/conda-bld/pytorch_1729647429097/work/torch/csrc/utils/tensor_numpy.cpp:206.)
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+ uimg = torch.from_numpy(img["unnormalized_img"]) / 255.0
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+ FPS: 8.640121356685011
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+ FPS: 9.060991890668788
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+ FPS: 9.258809561739502
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+ FPS: 9.30855702097904
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+ FPS: 9.342082210334143
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+ Database retrieval 2 : {0}
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+ FPS: 9.360102502877274
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+ FPS: 9.419028855520374
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+ FPS: 9.434152246730337
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+ FPS: 9.472562283492426
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+ FPS: 9.48588939395214
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+ Database retrieval 3 : {0}
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+ FPS: 9.433322319432957
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+ FPS: 9.435805134001011
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+ FPS: 9.444843986430934
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+ FPS: 9.44991854147795
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+ FPS: 9.428561580434693
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+ FPS: 9.436365572218465
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+ FPS: 9.4356452960193
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+ FPS: 9.437316043780312
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+ FPS: 9.4356806850056
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+ FPS: 9.442766823422096
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+ FPS: 9.449006664631693
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+ FPS: 9.46327105519892
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+ FPS: 9.471254749106961
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+ FPS: 9.480566628490411
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+ Database retrieval 6 : {3, 4}
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+ FPS: 9.46950189484681
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+ FPS: 9.483980953124401
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+ Database retrieval 7 : {3, 4}
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+ FPS: 9.472545433103797
44
+ FPS: 9.475964961933155
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+ FPS: 9.483812266728904
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+ FPS: 9.494100802014549
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+ FPS: 9.499581364332544
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+ Database retrieval 8 : {0, 2}
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+ FPS: 9.490432682433783
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+ Database retrieval 9 : {0, 1}
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+ FPS: 9.48628962733767
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+ FPS: 9.494942587512039
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+ FPS: 9.490830621806513
54
+ Database retrieval 10 : {5, 6}
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+ FPS: 9.385985733764004
93
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