File "/home/mona/BundleSDF/nerf_runner.py", line 1511, in mesh_texture_from_train_images locations, distance, index_tri = trimesh.proximity.closest_point(mesh, pts) File "/opt/conda/envs/py38/lib/python3.8/site-packages/trimesh/proximity.py", line 153, in closest_point all_candidates = np.concatenate(candidates) File "<__array_function__ internals>", line 200, in concatenate ValueError: need at least one array to concatenate #118

monajalal · 2023-11-15T19:10:32Z

I get an error in this step. How should I fix this?

(py38) root@ada:/home/mona/BundleSDF# python run_custom.py --mode run_video --video_dir /home/mona/BundleSDF/cup --out_folder /home/mona/BundleSDF/cup/out --use_segmenter 1 --use_gui 1 --debug_level 2


[nerf_runner.py] Iter: 0, valid_samples: 621027/655360, valid_rays: 1964/2048, loss: 28.6001949, rgb_loss: 22.5497475, rgb0_loss: 0.0000000, fs_rgb_loss: 0.0000000, depth_loss: 0.0000000, depth_loss0: 0.0000000, fs_loss: 0.0145888, point_cloud_loss: 0.0000000, point_cloud_normal_loss: 0.0000000, sdf_loss: 5.9261217, eikonal_loss: 0.0000000, variation_loss: 0.0000000, truncation(meter): 0.0100000, pose_reg: 0.0000000, reg_features: 0.1097347, 

[nerf_runner.py] train progress 200/2001
[nerf_runner.py] train progress 400/2001
[nerf_runner.py] train progress 600/2001
[nerf_runner.py] train progress 800/2001
[nerf_runner.py] train progress 1000/2001
[nerf_runner.py] train progress 1200/2001
[nerf_runner.py] train progress 1400/2001
[nerf_runner.py] train progress 1600/2001
[nerf_runner.py] train progress 1800/2001
[nerf_runner.py] train progress 2000/2001
cp: cannot stat '/home/mona/BundleSDF/cup/out//nerf_with_bundletrack_online/image_step_*.png': No such file or directory
[nerf_runner.py] query_pts:torch.Size([166375, 3]), valid:155595
[nerf_runner.py] Running Marching Cubes
[nerf_runner.py] done V:(11050, 3), F:(21770, 3)
[acceleratesupport.py] OpenGL_accelerate module loaded
[arraydatatype.py] Using accelerated ArrayDatatype
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/home/mona/BundleSDF/nerf_runner.py:1530: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  uvs_unique = torch.stack((uvs_flat_unique%(W-1), uvs_flat_unique//(W-1)), dim=-1).reshape(-1,2)
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Traceback (most recent call last):
  File "run_custom.py", line 223, in <module>
    run_one_video(video_dir=args.video_dir, out_folder=args.out_folder, use_segmenter=args.use_segmenter, use_gui=args.use_gui)
  File "run_custom.py", line 107, in run_one_video
    run_one_video_global_nerf(out_folder=out_folder)
  File "run_custom.py", line 152, in run_one_video_global_nerf
    tracker.run_global_nerf(reader=reader, get_texture=True, tex_res=512)
  File "/home/mona/BundleSDF/bundlesdf.py", line 790, in run_global_nerf
    mesh = nerf.mesh_texture_from_train_images(mesh, rgbs_raw=rgbs_raw, train_texture=False, tex_res=tex_res)
  File "/home/mona/BundleSDF/nerf_runner.py", line 1511, in mesh_texture_from_train_images
    locations, distance, index_tri = trimesh.proximity.closest_point(mesh, pts)
  File "/opt/conda/envs/py38/lib/python3.8/site-packages/trimesh/proximity.py", line 153, in closest_point
    all_candidates = np.concatenate(candidates)
  File "<__array_function__ internals>", line 200, in concatenate
ValueError: need at least one array to concatenate
Process Process-5:
Traceback (most recent call last):
  File "/opt/conda/envs/py38/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap
    self.run()
  File "/opt/conda/envs/py38/lib/python3.8/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/mona/BundleSDF/bundlesdf.py", line 89, in run_nerf
    join = p_dict['join']
  File "<string>", line 2, in __getitem__
  File "/opt/conda/envs/py38/lib/python3.8/multiprocessing/managers.py", line 835, in _callmethod
    kind, result = conn.recv()
  File "/opt/conda/envs/py38/lib/python3.8/multiprocessing/connection.py", line 250, in recv
    buf = self._recv_bytes()
  File "/opt/conda/envs/py38/lib/python3.8/multiprocessing/connection.py", line 414, in _recv_bytes
    buf = self._recv(4)
  File "/opt/conda/envs/py38/lib/python3.8/multiprocessing/connection.py", line 379, in _recv
    chunk = read(handle, remaining)
ConnectionResetError: [Errno 104] Connection reset by peer
[2023-11-15 11:02:27.858] [warning] [Bundler.cpp:59] Destructor
[2023-11-15 11:02:27.861] [warning] [Bundler.cpp:59] Destructor

I am following ros realsense depth aligned capture and use xmem for segmentation.

Here is one of the depth images:

(base) mona@ada:~/BundleSDF/cup/depth$ identify -verbose 1700072993.020334.png
Image:
  Filename: 1700072993.020334.png
  Format: PNG (Portable Network Graphics)
  Mime type: image/png
  Class: DirectClass
  Geometry: 640x480+0+0
  Units: Undefined
  Colorspace: Gray
  Type: Grayscale
  Base type: Undefined
  Endianness: Undefined
  Depth: 16-bit
  Channel depth:
    gray: 16-bit
  Channel statistics:
    Pixels: 307200
    Gray:
      min: 0  (0)
      max: 3776 (0.0576181)
      mean: 1099.04 (0.0167702)
      standard deviation: 646.808 (0.00986966)
      kurtosis: 1.31164
      skewness: 0.991703
      entropy: 0.919741
  Colors: 898
  Histogram:
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    158: (2271,2271,2271) #08DF08DF08DF gray(3.46532%)
    182: (2279,2279,2279) #08E708E708E7 gray(3.47753%)
    156: (2288,2288,2288) #08F008F008F0 gray(3.49126%)
    217: (2296,2296,2296) #08F808F808F8 gray(3.50347%)
    130: (2305,2305,2305) #090109010901 gray(3.5172%)
    222: (2314,2314,2314) #090A090A090A gray(3.53094%)
    129: (2322,2322,2322) #091209120912 gray(3.54314%)
    156: (2331,2331,2331) #091B091B091B gray(3.55688%)
    224: (2340,2340,2340) #092409240924 gray(3.57061%)
    175: (2349,2349,2349) #092D092D092D gray(3.58434%)
    180: (2358,2358,2358) #093609360936 gray(3.59808%)
    222: (2367,2367,2367) #093F093F093F gray(3.61181%)
    263: (2376,2376,2376) #094809480948 gray(3.62554%)
    222: (2385,2385,2385) #095109510951 gray(3.63928%)
    215: (2395,2395,2395) #095B095B095B gray(3.65454%)
    265: (2404,2404,2404) #096409640964 gray(3.66827%)
    207: (2413,2413,2413) #096D096D096D gray(3.682%)
    239: (2423,2423,2423) #097709770977 gray(3.69726%)
    250: (2433,2433,2433) #098109810981 gray(3.71252%)
    298: (2442,2442,2442) #098A098A098A gray(3.72625%)
    254: (2452,2452,2452) #099409940994 gray(3.74151%)
    306: (2462,2462,2462) #099E099E099E gray(3.75677%)
    355: (2472,2472,2472) #09A809A809A8 gray(3.77203%)
    880: (2482,2482,2482) #09B209B209B2 gray(3.78729%)
    1019: (2492,2492,2492) #09BC09BC09BC gray(3.80255%)
    541: (2502,2502,2502) #09C609C609C6 gray(3.81781%)
    461: (2512,2512,2512) #09D009D009D0 gray(3.83307%)
    466: (2522,2522,2522) #09DA09DA09DA gray(3.84833%)
    468: (2533,2533,2533) #09E509E509E5 gray(3.86511%)
    485: (2543,2543,2543) #09EF09EF09EF gray(3.88037%)
    447: (2554,2554,2554) #09FA09FA09FA gray(3.89715%)
    422: (2564,2564,2564) #0A040A040A04 gray(3.91241%)
    504: (2575,2575,2575) #0A0F0A0F0A0F gray(3.9292%)
    500: (2586,2586,2586) #0A1A0A1A0A1A gray(3.94598%)
    677: (2597,2597,2597) #0A250A250A25 gray(3.96277%)
    569: (2608,2608,2608) #0A300A300A30 gray(3.97955%)
    586: (2619,2619,2619) #0A3B0A3B0A3B gray(3.99634%)
    568: (2630,2630,2630) #0A460A460A46 gray(4.01312%)
    719: (2641,2641,2641) #0A510A510A51 gray(4.02991%)
    853: (2653,2653,2653) #0A5D0A5D0A5D gray(4.04822%)
    527: (2664,2664,2664) #0A680A680A68 gray(4.065%)
    453: (2676,2676,2676) #0A740A740A74 gray(4.08331%)
    342: (2688,2688,2688) #0A800A800A80 gray(4.10163%)
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    351: (2711,2711,2711) #0A970A970A97 gray(4.13672%)
    492: (2723,2723,2723) #0AA30AA30AA3 gray(4.15503%)
    538: (2735,2735,2735) #0AAF0AAF0AAF gray(4.17334%)
    606: (2747,2747,2747) #0ABB0ABB0ABB gray(4.19165%)
    492: (2760,2760,2760) #0AC80AC80AC8 gray(4.21149%)
    511: (2772,2772,2772) #0AD40AD40AD4 gray(4.2298%)
    412: (2785,2785,2785) #0AE10AE10AE1 gray(4.24964%)
    424: (2797,2797,2797) #0AED0AED0AED gray(4.26795%)
    340: (2810,2810,2810) #0AFA0AFA0AFA gray(4.28779%)
    297: (2823,2823,2823) #0B070B070B07 gray(4.30762%)
    170: (2836,2836,2836) #0B140B140B14 gray(4.32746%)
    139: (2849,2849,2849) #0B210B210B21 gray(4.3473%)
    74: (2863,2863,2863) #0B2F0B2F0B2F gray(4.36866%)
    33: (2876,2876,2876) #0B3C0B3C0B3C gray(4.38849%)
    47: (2889,2889,2889) #0B490B490B49 gray(4.40833%)
    36: (2903,2903,2903) #0B570B570B57 gray(4.42969%)
    45: (2917,2917,2917) #0B650B650B65 gray(4.45106%)
    35: (2931,2931,2931) #0B730B730B73 gray(4.47242%)
    40: (2945,2945,2945) #0B810B810B81 gray(4.49378%)
    32: (2959,2959,2959) #0B8F0B8F0B8F gray(4.51514%)
    44: (2973,2973,2973) #0B9D0B9D0B9D gray(4.53651%)
    30: (2988,2988,2988) #0BAC0BAC0BAC gray(4.5594%)
    30: (3002,3002,3002) #0BBA0BBA0BBA gray(4.58076%)
    47: (3017,3017,3017) #0BC90BC90BC9 gray(4.60365%)
    33: (3032,3032,3032) #0BD80BD80BD8 gray(4.62654%)
    20: (3047,3047,3047) #0BE70BE70BE7 gray(4.64942%)
    18: (3062,3062,3062) #0BF60BF60BF6 gray(4.67231%)
    32: (3077,3077,3077) #0C050C050C05 gray(4.6952%)
    22: (3093,3093,3093) #0C150C150C15 gray(4.71962%)
    29: (3108,3108,3108) #0C240C240C24 gray(4.7425%)
    33: (3124,3124,3124) #0C340C340C34 gray(4.76692%)
    19: (3140,3140,3140) #0C440C440C44 gray(4.79133%)
    21: (3156,3156,3156) #0C540C540C54 gray(4.81575%)
    29: (3172,3172,3172) #0C640C640C64 gray(4.84016%)
    26: (3189,3189,3189) #0C750C750C75 gray(4.8661%)
    26: (3205,3205,3205) #0C850C850C85 gray(4.89052%)
    28: (3222,3222,3222) #0C960C960C96 gray(4.91646%)
    20: (3239,3239,3239) #0CA70CA70CA7 gray(4.9424%)
    23: (3256,3256,3256) #0CB80CB80CB8 gray(4.96834%)
    32: (3274,3274,3274) #0CCA0CCA0CCA gray(4.9958%)
    30: (3291,3291,3291) #0CDB0CDB0CDB gray(5.02174%)
    56: (3309,3309,3309) #0CED0CED0CED gray(5.04921%)
    22: (3327,3327,3327) #0CFF0CFF0CFF gray(5.07668%)
    31: (3345,3345,3345) #0D110D110D11 gray(5.10414%)
    48: (3363,3363,3363) #0D230D230D23 gray(5.13161%)
    50: (3382,3382,3382) #0D360D360D36 gray(5.1606%)
    42: (3400,3400,3400) #0D480D480D48 gray(5.18807%)
    44: (3419,3419,3419) #0D5B0D5B0D5B gray(5.21706%)
    55: (3438,3438,3438) #0D6E0D6E0D6E gray(5.24605%)
    44: (3458,3458,3458) #0D820D820D82 gray(5.27657%)
    42: (3477,3477,3477) #0D950D950D95 gray(5.30556%)
    62: (3497,3497,3497) #0DA90DA90DA9 gray(5.33608%)
    51: (3517,3517,3517) #0DBD0DBD0DBD gray(5.3666%)
    52: (3537,3537,3537) #0DD10DD10DD1 gray(5.39712%)
    60: (3557,3557,3557) #0DE50DE50DE5 gray(5.42763%)
    57: (3578,3578,3578) #0DFA0DFA0DFA gray(5.45968%)
    60: (3599,3599,3599) #0E0F0E0F0E0F gray(5.49172%)
    43: (3620,3620,3620) #0E240E240E24 gray(5.52377%)
    46: (3642,3642,3642) #0E3A0E3A0E3A gray(5.55734%)
    51: (3663,3663,3663) #0E4F0E4F0E4F gray(5.58938%)
    66: (3685,3685,3685) #0E650E650E65 gray(5.62295%)
    160: (3707,3707,3707) #0E7B0E7B0E7B gray(5.65652%)
    191: (3730,3730,3730) #0E920E920E92 gray(5.69162%)
    136: (3753,3753,3753) #0EA90EA90EA9 gray(5.72671%)
    40: (3776,3776,3776) #0EC00EC00EC0 gray(5.76181%)
  Rendering intent: Undefined
  Gamma: 0.454545
  Background color: gray(255)
  Border color: gray(223)
  Matte color: gray(189)
  Transparent color: gray(0)
  Interlace: None
  Intensity: Undefined
  Compose: Over
  Page geometry: 640x480+0+0
  Dispose: Undefined
  Iterations: 0
  Compression: Zip
  Orientation: Undefined
  Properties:
    date:create: 2023-11-15T18:37:03+00:00
    date:modify: 2023-11-15T18:37:03+00:00
    png:IHDR.bit-depth-orig: 16
    png:IHDR.bit_depth: 16
    png:IHDR.color-type-orig: 0
    png:IHDR.color_type: 0 (Grayscale)
    png:IHDR.interlace_method: 0 (Not interlaced)
    png:IHDR.width,height: 640, 480
    signature: 9342afc9aeb34eb6dbc1651ed881b82f8cd3890eccfcb6fd096f5ce62d7adf68
  Artifacts:
    filename: 1700072993.020334.png
    verbose: true
  Tainted: False
  Filesize: 125602B
  Number pixels: 307200
  Pixels per second: 48.6842MB
  User time: 0.010u
  Elapsed time: 0:01.006
  Version: ImageMagick 6.9.11-60 Q16 x86_64 2021-01-25 https://imagemagick.org

here is the mesh_cleaned.obj that is created that doesn't resemble an espresso cup.

The text was updated successfully, but these errors were encountered:

wenbowen123 · 2023-11-19T22:15:47Z

duplicate of #111

monajalal · 2023-11-21T18:47:33Z

@wenbowen123 this is not a duplicate. For me, it does happen in the first step. Also, my masks have no problem. Could you please open the issue? I will close it once it gets fixed.

wenbowen123 · 2023-11-21T19:26:20Z

@monajalal have you tried the latest commit as mentioned in #111 ?

monajalal · 2023-11-21T19:32:00Z

yes it worked (I didn't get the error in second step though and got it in first step)

[bundlesdf.py] frame_pairs: 1
[loftr_wrapper.py] image0: torch.Size([1, 1, 400, 400])
[loftr_wrapper.py] net forward
[loftr_wrapper.py] mconf, 0.2004760205745697 0.9993301630020142
[loftr_wrapper.py] pair_ids (1373,)
[loftr_wrapper.py] corres: (1373, 5)
[2023-11-21 11:19:00.265] [warning] [FeatureManager.cpp:1589] start multi pair ransac GPU, pairs#=1
[2023-11-21 11:19:00.266] [warning] [FeatureManager.cpp:1699] ransac makes match betwee frame 1700074151.983942 1700074151.917228 #inliers=687, #prev 1187
[bundlesdf.py] frame 1700074151.983942 pose update before
[[ 0.11  -0.988  0.107  0.136]
 [-0.774 -0.152 -0.615  0.931]
 [ 0.624 -0.015 -0.781  0.868]
 [ 0.     0.     0.     1.   ]]
[2023-11-21 11:19:00.268] [warning] [FeatureManager.cpp:1095] procrustesByCorrespondence err per point between 1700074151.983942 and 1700074151.917228: 0.000204096
[bundlesdf.py] frame 1700074151.983942 pose update after
[[ 0.096 -0.995  0.022  0.237]
 [-0.757 -0.087 -0.647  0.954]
 [ 0.646  0.046 -0.762  0.836]
 [ 0.     0.     0.     1.   ]]
[2023-11-21 11:19:00.269] [warning] [Bundler.cpp:67] forgetting frame 1700074150.182662
[2023-11-21 11:19:00.269] [warning] [FeatureManager.cpp:469] forgetting frame 1700074150.182662
[bundlesdf.py] exceed window size, forget frame 1700074150.182662
[2023-11-21 11:19:00.272] [warning] [Bundler.cpp:435] total keyframes=187, want to select 10
[2023-11-21 11:19:00.300] [warning] [Bundler.cpp:516] ids#=187, max_BA_frames-frames.size()=9
[2023-11-21 11:19:00.300] [warning] [Bundler.cpp:525] frames#=10
[2023-11-21 11:19:00.301] [warning] [Bundler.cpp:793] frame 1700074150.115947 and 1700074149.849090 visible=0.811631
[2023-11-21 11:19:00.301] [warning] [Bundler.cpp:802] add frame (1700074150.115947, 1700074149.849090) into pairs
[2023-11-21 11:19:00.301] [warning] [Bundler.cpp:793] frame 1700074150.449519 and 1700074149.849090 visible=0.923981
[2023-11-21 11:19:00.301] [warning] [Bundler.cpp:802] add frame (1700074150.449519, 1700074149.849090) into pairs
[2023-11-21 11:19:00.301] [warning] [Bundler.cpp:793] frame 1700074151.983942 and 1700074149.849090 visible=0.914919
[2023-11-21 11:19:00.301] [warning] [Bundler.cpp:802] add frame (1700074151.983942, 1700074149.849090) into pairs
[2023-11-21 11:19:00.301] [warning] [Bundler.cpp:793] frame 1700074150.115947 and 1700074149.915804 visible=0.838338
[2023-11-21 11:19:00.301] [warning] [Bundler.cpp:802] add frame (1700074150.115947, 1700074149.915804) into pairs
[2023-11-21 11:19:00.302] [warning] [Bundler.cpp:793] frame 1700074150.449519 and 1700074149.915804 visible=0.905004
[2023-11-21 11:19:00.302] [warning] [Bundler.cpp:802] add frame (1700074150.449519, 1700074149.915804) into pairs
[2023-11-21 11:19:00.302] [warning] [Bundler.cpp:793] frame 1700074151.983942 and 1700074149.915804 visible=0.932489
[2023-11-21 11:19:00.302] [warning] [Bundler.cpp:802] add frame (1700074151.983942, 1700074149.915804) into pairs
[2023-11-21 11:19:00.302] [warning] [Bundler.cpp:793] frame 1700074150.115947 and 1700074149.982519 visible=0.8317
[2023-11-21 11:19:00.302] [warning] [Bundler.cpp:802] add frame (1700074150.115947, 1700074149.982519) into pairs
[2023-11-21 11:19:00.302] [warning] [Bundler.cpp:793] frame 1700074150.449519 and 1700074149.982519 visible=0.846832
[2023-11-21 11:19:00.302] [warning] [Bundler.cpp:802] add frame (1700074150.449519, 1700074149.982519) into pairs
[2023-11-21 11:19:00.302] [warning] [Bundler.cpp:793] frame 1700074151.983942 and 1700074149.982519 visible=0.949846
[2023-11-21 11:19:00.302] [warning] [Bundler.cpp:802] add frame (1700074151.983942, 1700074149.982519) into pairs
[2023-11-21 11:19:00.303] [warning] [Bundler.cpp:793] frame 1700074150.249376 and 1700074150.115947 visible=0.775355
[2023-11-21 11:19:00.303] [warning] [Bundler.cpp:802] add frame (1700074150.249376, 1700074150.115947) into pairs
[2023-11-21 11:19:00.303] [warning] [Bundler.cpp:793] frame 1700074150.449519 and 1700074150.115947 visible=0.737151
[2023-11-21 11:19:00.303] [warning] [Bundler.cpp:802] add frame (1700074150.449519, 1700074150.115947) into pairs
[2023-11-21 11:19:00.303] [warning] [Bundler.cpp:793] frame 1700074151.316803 and 1700074150.115947 visible=0.875138
[2023-11-21 11:19:00.303] [warning] [Bundler.cpp:802] add frame (1700074151.316803, 1700074150.115947) into pairs
[2023-11-21 11:19:00.303] [warning] [Bundler.cpp:793] frame 1700074151.516945 and 1700074150.115947 visible=0.881712
[2023-11-21 11:19:00.303] [warning] [Bundler.cpp:802] add frame (1700074151.516945, 1700074150.115947) into pairs
[2023-11-21 11:19:00.304] [warning] [Bundler.cpp:793] frame 1700074151.650373 and 1700074150.115947 visible=0.859779
[2023-11-21 11:19:00.304] [warning] [Bundler.cpp:802] add frame (1700074151.650373, 1700074150.115947) into pairs
[2023-11-21 11:19:00.304] [warning] [Bundler.cpp:793] frame 1700074151.983942 and 1700074150.115947 visible=0.935683
[2023-11-21 11:19:00.304] [warning] [Bundler.cpp:802] add frame (1700074151.983942, 1700074150.115947) into pairs
[2023-11-21 11:19:00.304] [warning] [Bundler.cpp:793] frame 1700074150.449519 and 1700074150.249376 visible=0.767536
[2023-11-21 11:19:00.304] [warning] [Bundler.cpp:802] add frame (1700074150.449519, 1700074150.249376) into pairs
[2023-11-21 11:19:00.304] [warning] [Bundler.cpp:793] frame 1700074151.983942 and 1700074150.249376 visible=0.946971
[2023-11-21 11:19:00.304] [warning] [Bundler.cpp:802] add frame (1700074151.983942, 1700074150.249376) into pairs
[2023-11-21 11:19:00.305] [warning] [Bundler.cpp:793] frame 1700074151.316803 and 1700074150.449519 visible=0.90125
[2023-11-21 11:19:00.305] [warning] [Bundler.cpp:802] add frame (1700074151.316803, 1700074150.449519) into pairs
[2023-11-21 11:19:00.305] [warning] [Bundler.cpp:793] frame 1700074151.516945 and 1700074150.449519 visible=0.907337
[2023-11-21 11:19:00.305] [warning] [Bundler.cpp:802] add frame (1700074151.516945, 1700074150.449519) into pairs
[2023-11-21 11:19:00.306] [warning] [Bundler.cpp:793] frame 1700074151.650373 and 1700074150.449519 visible=0.905742
[2023-11-21 11:19:00.306] [warning] [Bundler.cpp:802] add frame (1700074151.650373, 1700074150.449519) into pairs
[2023-11-21 11:19:00.306] [warning] [Bundler.cpp:793] frame 1700074151.983942 and 1700074150.449519 visible=0.889362
[2023-11-21 11:19:00.306] [warning] [Bundler.cpp:802] add frame (1700074151.983942, 1700074150.449519) into pairs
[2023-11-21 11:19:00.306] [warning] [Bundler.cpp:793] frame 1700074151.983942 and 1700074151.316803 visible=0.888084
[2023-11-21 11:19:00.306] [warning] [Bundler.cpp:802] add frame (1700074151.983942, 1700074151.316803) into pairs
[2023-11-21 11:19:00.306] [warning] [Bundler.cpp:793] frame 1700074151.983942 and 1700074151.516945 visible=0.891172
[2023-11-21 11:19:00.306] [warning] [Bundler.cpp:802] add frame (1700074151.983942, 1700074151.516945) into pairs
[2023-11-21 11:19:00.306] [warning] [Bundler.cpp:793] frame 1700074151.983942 and 1700074151.650373 visible=0.890534
[2023-11-21 11:19:00.306] [warning] [Bundler.cpp:802] add frame (1700074151.983942, 1700074151.650373) into pairs
[bundlesdf.py] frame_pairs: 24
[2023-11-21 11:19:00.306] [warning] [FeatureManager.cpp:2690] _raw_matches found exsting pair (1700074150.115947, 1700074149.849090)
[2023-11-21 11:19:00.307] [warning] [FeatureManager.cpp:2690] _raw_matches found exsting pair (1700074150.449519, 1700074149.849090)
[2023-11-21 11:19:00.308] [warning] [FeatureManager.cpp:2690] _raw_matches found exsting pair (1700074150.115947, 1700074149.915804)
[2023-11-21 11:19:00.308] [warning] [FeatureManager.cpp:2690] _raw_matches found exsting pair (1700074150.449519, 1700074149.915804)
[2023-11-21 11:19:00.309] [warning] [FeatureManager.cpp:2690] _raw_matches found exsting pair (1700074150.115947, 1700074149.982519)
[2023-11-21 11:19:00.309] [warning] [FeatureManager.cpp:2690] _raw_matches found exsting pair (1700074150.449519, 1700074149.982519)
[2023-11-21 11:19:00.311] [warning] [FeatureManager.cpp:2690] _raw_matches found exsting pair (1700074150.249376, 1700074150.115947)
[2023-11-21 11:19:00.311] [warning] [FeatureManager.cpp:2690] _raw_matches found exsting pair (1700074150.449519, 1700074150.115947)
[2023-11-21 11:19:00.311] [warning] [FeatureManager.cpp:2690] _raw_matches found exsting pair (1700074151.316803, 1700074150.115947)
[2023-11-21 11:19:00.311] [warning] [FeatureManager.cpp:2690] _raw_matches found exsting pair (1700074151.516945, 1700074150.115947)
[2023-11-21 11:19:00.311] [warning] [FeatureManager.cpp:2690] _raw_matches found exsting pair (1700074151.650373, 1700074150.115947)
[2023-11-21 11:19:00.312] [warning] [FeatureManager.cpp:2690] _raw_matches found exsting pair (1700074150.449519, 1700074150.249376)
[loftr_wrapper.py] image0: torch.Size([12, 1, 400, 400])
[loftr_wrapper.py] net forward
[loftr_wrapper.py] mconf, 0.20008717477321625 0.9882730841636658
[loftr_wrapper.py] pair_ids (2947,)
[loftr_wrapper.py] corres: (2947, 5)
[2023-11-21 11:19:00.457] [warning] [FeatureManager.cpp:1589] start multi pair ransac GPU, pairs#=12
[2023-11-21 11:19:00.484] [warning] [FeatureManager.cpp:1695] after ransac, frame 1700074151.983942 and 1700074149.849090 has too few matches #0, ignore
[2023-11-21 11:19:00.484] [warning] [FeatureManager.cpp:1695] after ransac, frame 1700074151.983942 and 1700074149.915804 has too few matches #0, ignore
[2023-11-21 11:19:00.484] [warning] [FeatureManager.cpp:1695] after ransac, frame 1700074151.983942 and 1700074149.982519 has too few matches #3, ignore
[2023-11-21 11:19:00.484] [warning] [FeatureManager.cpp:1699] ransac makes match betwee frame 1700074151.983942 1700074150.115947 #inliers=7, #prev 153
[2023-11-21 11:19:00.484] [warning] [FeatureManager.cpp:1699] ransac makes match betwee frame 1700074151.983942 1700074150.249376 #inliers=19, #prev 197
[2023-11-21 11:19:00.484] [warning] [FeatureManager.cpp:1695] after ransac, frame 1700074151.316803 and 1700074150.449519 has too few matches #2, ignore
[2023-11-21 11:19:00.484] [warning] [FeatureManager.cpp:1695] after ransac, frame 1700074151.516945 and 1700074150.449519 has too few matches #3, ignore
[2023-11-21 11:19:00.484] [warning] [FeatureManager.cpp:1695] after ransac, frame 1700074151.650373 and 1700074150.449519 has too few matches #4, ignore
[2023-11-21 11:19:00.484] [warning] [FeatureManager.cpp:1699] ransac makes match betwee frame 1700074151.983942 1700074150.449519 #inliers=6, #prev 179
[2023-11-21 11:19:00.484] [warning] [FeatureManager.cpp:1699] ransac makes match betwee frame 1700074151.983942 1700074151.316803 #inliers=86, #prev 280
[2023-11-21 11:19:00.484] [warning] [FeatureManager.cpp:1699] ransac makes match betwee frame 1700074151.983942 1700074151.516945 #inliers=11, #prev 194
[2023-11-21 11:19:00.484] [warning] [FeatureManager.cpp:1699] ransac makes match betwee frame 1700074151.983942 1700074151.650373 #inliers=77, #prev 363
#optimizeGPU frames=10, #keyframes=187, #_frames=193
1700074149.849090 1700074149.915804 1700074149.982519 1700074150.115947 1700074150.249376 1700074150.449519 1700074151.316803 1700074151.516945 1700074151.650373 1700074151.983942 
[2023-11-21 11:19:00.486] [warning] [Bundler.cpp:920] OptimizerGPU begin, global_corres#=206
global_corres=206
maxNumResiduals / maxNumberOfImages = 216206 / 10 = 21620
m_maxNumberOfImages*m_maxCorrPerImage = 10 x 206 = 2060
m_solver->solve Time difference = 4.021[ms]
[2023-11-21 11:19:00.496] [warning] [Bundler.cpp:924] OptimizerGPU finish
[2023-11-21 11:19:00.496] [warning] [Bundler.cpp:302] frame 1700074151.983942 not selected as keyframe since its rot diff with frame 1700074151.516945 is 3.48706 deg
[bundlesdf.py] processNewFrame done 1700074151.983942
[bundlesdf.py] rematch_after_nerf: True
[2023-11-21 11:19:00.497] [warning] [Bundler.cpp:961] Welcome saveNewframeResult
[2023-11-21 11:19:00.532] [warning] [Bundler.cpp:1110] saveNewframeResult done
[2023-11-21 11:19:04.973] [warning] [Bundler.cpp:49] Connected to nerf_port 9999
[2023-11-21 11:19:04.973] [warning] [FeatureManager.cpp:2084] Connected to port 5555
default_cfg {'backbone_type': 'ResNetFPN', 'resolution': (8, 2), 'fine_window_size': 5, 'fine_concat_coarse_feat': True, 'resnetfpn': {'initial_dim': 128, 'block_dims': [128, 196, 256]}, 'coarse': {'d_model': 256, 'd_ffn': 256, 'nhead': 8, 'layer_names': ['self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross'], 'attention': 'linear', 'temp_bug_fix': False}, 'match_coarse': {'thr': 0.2, 'border_rm': 2, 'match_type': 'dual_softmax', 'dsmax_temperature': 0.1, 'skh_iters': 3, 'skh_init_bin_score': 1.0, 'skh_prefilter': True, 'train_coarse_percent': 0.4, 'train_pad_num_gt_min': 200}, 'fine': {'d_model': 128, 'd_ffn': 128, 'nhead': 8, 'layer_names': ['self', 'cross'], 'attention': 'linear'}}
[bundlesdf.py] last_stamp 1700074151.983942
[bundlesdf.py] keyframes#: 187
[tool.py] compute_scene_bounds_worker start
[tool.py] compute_scene_bounds_worker done
[tool.py] merge pcd
[tool.py] compute_translation_scales done
translation_cvcam=[-0.00473613 -0.00114112  0.01014452], sc_factor=2.0600853021701306
[nerf_runner.py] Octree voxel dilate_radius:1
level 0, resolution: 16
level 1, resolution: 20
level 2, resolution: 24
level 3, resolution: 28
level 4, resolution: 34
level 5, resolution: 41
level 6, resolution: 49
level 7, resolution: 59
level 8, resolution: 71
level 9, resolution: 85
level 10, resolution: 102
level 11, resolution: 123
level 12, resolution: 148
level 13, resolution: 177
level 14, resolution: 213
level 15, resolution: 256
GridEncoder: input_dim=3 n_levels=16 level_dim=2 resolution=16 -> 256 per_level_scale=1.2030 params=(20411696, 2) gridtype=hash align_corners=False
sc_factor 2.0600853021701306
translation [-0.00473613 -0.00114112  0.01014452]
[nerf_runner.py] denoise cloud
[nerf_runner.py] Denoising rays based on octree cloud
[nerf_runner.py] bad_mask#=47409
rays torch.Size([8815005, 12])
Start training
[nerf_runner.py] train progress 0/2001
[nerf_runner.py] Iter: 0, valid_samples: 654784/655360, valid_rays: 2047/2048, loss: 19.2875576, rgb_loss: 18.9341164, rgb0_loss: 0.0000000, fs_rgb_loss: 0.0000000, depth_loss: 0.0000000, depth_loss0: 0.0000000, fs_loss: 0.0718253, point_cloud_loss: 0.0000000, point_cloud_normal_loss: 0.0000000, sdf_loss: 0.1769290, eikonal_loss: 0.0000000, variation_loss: 0.0000000, truncation(meter): 0.0100000, pose_reg: 0.0000000, reg_features: 0.1046863, 

[nerf_runner.py] train progress 200/2001
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[nerf_runner.py] train progress 2000/2001
cp: cannot stat '/home/mona/BundleSDF/pallet/out//nerf_with_bundletrack_online/image_step_*.png': No such file or directory
[nerf_runner.py] query_pts:torch.Size([114084125, 3]), valid:45562520
[nerf_runner.py] Running Marching Cubes
[nerf_runner.py] done V:(86787, 3), F:(171710, 3)
[acceleratesupport.py] OpenGL_accelerate module loaded
[arraydatatype.py] Using accelerated ArrayDatatype
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/home/mona/BundleSDF/nerf_runner.py:1532: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  uvs_unique = torch.stack((uvs_flat_unique%(W-1), uvs_flat_unique//(W-1)), dim=-1).reshape(-1,2)
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/home/mona/BundleSDF/nerf_runner.py:1539: RuntimeWarning: invalid value encountered in cast
  tex_image = np.clip(tex_image,0,255).astype(np.uint8)
Done
[2023-11-21 11:21:03.507] [warning] [Bundler.cpp:59] Destructor
[2023-11-21 11:21:03.909] [warning] [Bundler.cpp:59] Destructor

my created mesh looks very weird though (my object looks like a rectangular cuboid itself)

For now, I close this issue since the main problem is resolved.

Could you please tell what is the order you have used for your cam_K.txt file? Thank you so much for the new push to code.

monajalal · 2023-11-21T20:39:19Z

@wenbowen123 I think I got it figured IntelRealSense/realsense-ros#2935 (comment)

Wahaha-code · 2023-12-12T12:47:07Z

Hello, I encountered the same problem, how did you solve it（ValueError: need at least one array to concatenate）?

monajalal · 2023-12-12T15:25:50Z

@Wahaha-code you need depth-aligned depth and rgb capture. I used ros realsense for this purpose in a ros noetic docker. My camera is D435.

Wahaha-code · 2023-12-12T15:33:06Z

Thank you for your guidance. In fact, I have always had a problem. During my reconstruction process, which is the first step, due to the small number of matching points between the two frames, it will often be interrupted. I have to press the c key frequently to continue running. . I want to know how I can better collect my data. At the same time, I found that when using xmem to split image files, frames often skip. Is this the cause of this problem? I don't know how to solve this problem.

wenbowen123 closed this as completed Nov 19, 2023

wenbowen123 reopened this Nov 21, 2023

monajalal closed this as completed Nov 21, 2023

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