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Datasets:
Slighting3121
/
3D-Reconstruction-Benchmark-Asset-Inspection

Modalities:
3D
Languages:
English
Size:
10K<n<100K
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ReadError
Message:      invalid header
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1576, in _prepare_split_single
                  for key, record in generator:
                                     ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 609, in wrapped
                  for item in generator(*args, **kwargs):
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 120, in _generate_examples
                  for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
                                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 33, in _get_pipeline_from_tar
                  for filename, f in tar_iterator:
                                     ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/track.py", line 49, in __iter__
                  for x in self.generator(*self.args):
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1380, in _iter_from_urlpath
                  yield from cls._iter_tar(f)
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1331, in _iter_tar
                  stream = tarfile.open(fileobj=f, mode="r|*")
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/tarfile.py", line 1886, in open
                  t = cls(name, filemode, stream, **kwargs)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/tarfile.py", line 1762, in __init__
                  self.firstmember = self.next()
                                     ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/tarfile.py", line 2750, in next
                  raise ReadError(str(e)) from None
              tarfile.ReadError: invalid header
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1342, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1438, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1616, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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json
dict
__key__
string
__url__
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cam_parameters/00000518
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001044
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001046
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cam_parameters/00001047
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cam_parameters/00001049
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001050
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cam_parameters/00001058
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cam_parameters/00001059
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cam_parameters/00001060
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cam_parameters/00001061
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001062
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cam_parameters/00001063
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001064
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001065
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001066
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001067
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001068
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001069
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001070
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001071
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001072
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00001073
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000469
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000470
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000471
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000472
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000473
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000474
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000475
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000476
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000477
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000478
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000479
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000480
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000481
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000482
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000483
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000484
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000485
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000486
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000487
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000488
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000489
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000490
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000491
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000678
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000679
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000680
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000681
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000682
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000683
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000684
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000685
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000686
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000687
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000688
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000689
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000690
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000691
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000692
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000693
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000694
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000177
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000178
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000179
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000180
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000181
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000182
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
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cam_parameters/00000183
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -4.996850967407227, -5.048842430114746, 17.626712799072266 ], "rot": [ -0.7112635970115662, -0.45338261127471924, 0.28857889771461487, 0.4530678987503052 ], "sensor_width": 36 }
cam_parameters/00000184
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -4.987839221954346, -6.5639214515686035, 17.582284927368164 ], "rot": [ -0.764154314994812, -0.48777782917022705, 0.22652819752693176, 0.3561263382434845 ], "sensor_width": 36 }
cam_parameters/00000185
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -5.029750347137451, -7.488014221191406, 17.28449821472168 ], "rot": [ -0.7649013996124268, -0.48495084047317505, 0.22840166091918945, 0.3571851849555969 ], "sensor_width": 36 }
cam_parameters/00000186
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -4.970249652862549, -8.31069564819336, 17.088125228881836 ], "rot": [ -0.7638375163078308, -0.48896074295043945, 0.22573934495449066, 0.3556843400001526 ], "sensor_width": 36 }
cam_parameters/00000187
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -5.002714157104492, -9.225341796875, 16.799785614013672 ], "rot": [ -0.764420747756958, -0.48677587509155273, 0.22719405591487885, 0.35650113224983215 ], "sensor_width": 36 }
cam_parameters/00000188
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -5.0238871574401855, -10.128695487976074, 16.522737503051758 ], "rot": [ -0.764797568321228, -0.48534703254699707, 0.22814004123210907, 0.3570364713668823 ], "sensor_width": 36 }
cam_parameters/00000189
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -4.979261875152588, -10.966251373291016, 16.31148910522461 ], "rot": [ -0.7640000581741333, -0.48835495114326477, 0.22614368796348572, 0.35591059923171997 ], "sensor_width": 36 }
cam_parameters/00000190
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -5.0238871574401855, -11.893059730529785, 16.010988235473633 ], "rot": [ -0.764797568321228, -0.48534703254699707, 0.22814004123210907, 0.3570364713668823 ], "sensor_width": 36 }
cam_parameters/00000191
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -5.006297588348389, -12.757652282714844, 15.772703170776367 ], "rot": [ -0.7644846439361572, -0.4865342378616333, 0.22735439240932465, 0.35659167170524597 ], "sensor_width": 36 }
cam_parameters/00000192
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -4.970249652862549, -13.603785514831543, 15.552876472473145 ], "rot": [ -0.7638375163078308, -0.48896074295043945, 0.22573934495449066, 0.3556843400001526 ], "sensor_width": 36 }
cam_parameters/00000193
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -5.020738124847412, -14.536456108093262, 15.246513366699219 ], "rot": [ -0.7647416591644287, -0.48555973172187805, 0.22799953818321228, 0.3569567799568176 ], "sensor_width": 36 }
cam_parameters/00000194
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -5.003149032592773, -15.401047706604004, 14.84593677520752 ], "rot": [ -0.7644284963607788, -0.486746609210968, 0.22721350193023682, 0.35651203989982605 ], "sensor_width": 36 }
cam_parameters/00000195
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -5.003149032592773, -16.283231735229492, 14.42777156829834 ], "rot": [ -0.7644284963607788, -0.486746609210968, 0.22721350193023682, 0.35651203989982605 ], "sensor_width": 36 }
cam_parameters/00000196
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -4.9761128425598145, -17.13837432861328, 14.036641120910645 ], "rot": [ -0.7639433145523071, -0.48856666684150696, 0.226002499461174, 0.3558315634727478 ], "sensor_width": 36 }
cam_parameters/00000197
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -5.029750347137451, -18.074195861816406, 13.564839363098145 ], "rot": [ -0.7649013996124268, -0.48495084047317505, 0.22840166091918945, 0.3571851849555969 ], "sensor_width": 36 }
cam_parameters/00000198
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -4.997285842895508, -18.923913955688477, 13.179137229919434 ], "rot": [ -0.7643236517906189, -0.4871416985988617, 0.2269512265920639, 0.35636425018310547 ], "sensor_width": 36 }
cam_parameters/00000199
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -4.9851250648498535, -19.79393196105957, 12.77313232421875 ], "rot": [ -0.7641056180000305, -0.487960547208786, 0.22640648484230042, 0.35605791211128235 ], "sensor_width": 36 }
cam_parameters/00000200
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -4.996850967407227, -20.687841415405273, 12.343240737915039 ], "rot": [ -0.7643158435821533, -0.487170934677124, 0.22693176567554474, 0.3563533425331116 ], "sensor_width": 36 }
cam_parameters/00000201
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -4.996850967407227, -21.570022583007812, 11.925074577331543 ], "rot": [ -0.7643158435821533, -0.487170934677124, 0.22693176567554474, 0.3563533425331116 ], "sensor_width": 36 }
cam_parameters/00000202
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -4.987839221954346, -22.3255672454834, 11.284676551818848 ], "rot": [ -0.764154314994812, -0.48777782917022705, 0.22652819752693176, 0.3561263382434845 ], "sensor_width": 36 }
cam_parameters/00000203
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
{ "clip_end": 150, "clip_start": 0.10000000149011612, "focal_length": 50, "loc": [ -5.029750347137451, -23.132038116455078, 10.593354225158691 ], "rot": [ -0.7649013996124268, -0.48495084047317505, 0.22840166091918945, 0.3571851849555969 ], "sensor_width": 36 }
cam_parameters/00000204
hf://datasets/Slighting3121/3D-Reconstruction-Benchmark-Asset-Inspection@fe6a30722856b11648a5ab81f4c7349b07f7e00c/bridge/cam_parameters.tar
End of preview.

Asset Inspection Dataset

This dataset's scenes are arranged as follows:

scene/
├── cam_parameters.tar
├── depths.tar
├── images.tar
├── scene.glb Scene 
└── scene.blend

The office building scene has four surface soiling settings, so it is arranged as follows

office_building
├── cam_parameters.tar
├── depths.tar
├── high_soiling_images.tar
├── low_soiling_images.tar
├── medium_soiling_images.tar
├── office_building.glb
├── very_low_soiling_images.tar
└── Office_model.blend

Images

images.tar files and *_images.tar contain the images in the dataset. Noise has been added - refer to the paper for details on our additive noise model. The images in the bridge and crane scenes are .pngs and office building images are .jpgs to save space.

Depth Maps

depths contains the depth maps. These are .exr files which store the metric depth. Each pixel has a three values each of which is the depth. To open a .exr file, one can use the OpenEXR python library or the image viewer tev. A function for opening .exr files using OpenEXR in python is included below :

import OpenEXR

def open_exr(f_name: str):
    with OpenEXR.File(f_name) as infile:
        header = infile.header()
        channels = infile.channels()
        keys = list(channels.keys())
        if keys == ["RGB"]:
            img = channels["RGB"].pixels
        if keys == ["RGBA"]:
            img = channels["RGBA"].pixels
        elif keys == ["X", "Y", "Z"]:
            X = channels["X"].pixels
            Y = channels["Y"].pixels
            Z = channels["Z"].pixels

            height, width = Z.shape

            img = np.empty((height, width, 3))
            img[..., 0] = X
            img[..., 1] = Y
            img[..., 2] = Z

    return img, header

Each pixel of the depth map contains the metric depth in each of the three channels. Therefore, if we return to the previous code and img[..., 0] will get the depth for each pixel in the image.

Camera Intrinsics and Extrinsics

Camera intrinsics and extrinsics for each frame are included in cam_parameters. Each frame number has an individual .json file which is formatted as follows:

{
 "loc": [
  world_x_position,
  world_y_position,
  world_z_position
 ],
 "rot": [
    w,
    x,
    y,
    z
 ],
 "focal_length": f_mm,
 "sensor_width": sensor_width_mm,
 "clip_end": max_dist_m,
 "clip_start": min_dist_m
}

The loc field refers to the position of the camera in the blender world coordinate frame. The rot field refers to the rotation quaternion in the blender world coordinate frame (in scalar first format (w, x, y, z)). focal_length refers to the focal length of the camera in millimetres. sensor_width refers to the width of the camera sensor in mm. clip_start and clip_end are the minimum and maximum distance settings that blender renders.

To convert these coordinates into the familiar COLMAP coordinate system, one can use the following lines of code:

  from scipy.spatial.transform import Rotation

colmap_rot = [rot[1], rot[0],
              rot[3], -rot[2]]
colmap_transform = Rotation.from_quat(colmap_rot, scalar_first=True)
T_vec = -colmap_transform.apply(loc)

Mesh model

For each scene, we exported a mesh model. To produce a ground truth point cloud, we sampled the mesh using Open3D. To replicate this, one can use:

import open3d as o3d

mesh = o3d.io.read_triangle_mesh(glb_file_path)
pointcloud_sampled = mesh.sample_points_uniformly(
    number_of_points=1000000)

Blender Scenes

These blender scenes were constructed in Blender 5.0.1 from assets from BlenderKit. To render the office building with different soiling settings, use the render_office.py script which can be done using Blender's in built scripting capability or with the following shell command:

blender -b Office_model.blend --python render_office.py

Sources for Blender Assets

We used the following assets in our dataset (some of them substantially modified):

  1. Mirazev, A.: Model: Modern Building. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/bab4567f-7f71-44f7-af6b-96f5d60cb66e/, last accessed 09 Mar 2026
  2. Jacinto, M., Pinto, J., Patrikar, J., Keller, J., Cunha, R., Scherer, S., Pascoal, A.: Pegasus Simulator: An Isaac Sim Framework for Multiple Aerial Vehicles Simulation. In: 2024 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1–8. IEEE (2024). https://doi.org/10.1109/ICUAS60882.2024.10556959
  3. Se-Cam, J.: Model: 80s Cartoon Wagon Car. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/5150ca16-f5c0-49e9-b2a4-bdf9d6a51e1a/, last accessed 09 Mar 2026
  4. Se-Cam, J.: Model: Cartoon Car / Toy Car. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/b24f0fab-da30-4a85-bdec-c3b2fced3bb7/, last accessed 09 Mar 2026
  5. Se-Cam, J.: Model: 80s Cartoon Pick-Up. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/c587fce1-af5a-4f77-9e78-af10526cf4a5/, last accessed 09 Mar 2026
  6. Tirindelli, D.: Model: 6kw Solar Panels Structure. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/ba8093b7-416c-461c-af22-ac311df86b03/, last accessed 09 Mar 2026
  7. Rexodus: Model: Antenna. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/c67c9184-3ebd-4809-adf5-91b1dd0ae759/, last accessed 09 Mar 2026
  8. Mitroi, R.: Model: AC Unit Big. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/1a06752c-d093-474a-b870-a011deb2366e/, last accessed 09 Mar 2026
  9. Wells, C.: Material: Procedural Concrete Tiles. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/3b90f722-6d01-4f62-be56-f392e9312bac/, last accessed 09 Mar 2026
  10. Paludo, L.: Material: Simple Concrete Procedural. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/6156591b-e7a4-4aee-a6cc-0380517e4003/, last accessed 09 Mar 2026
  11. Share Textures: Material: Dark Steel. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/f170e0bf-f784-482e-86eb-1d7e307a22cc/, last accessed 09 Mar 2026
  12. Michaud, A.: Material: Textured Painted Wall (Procedural). BlenderKit, https://www.blenderkit.com/asset-gallery-detail/9891df1f-b429-461a-bc5e-b0520e25a120/, last accessed 09 Mar 2026
  13. Yang, R.: Material: White Wall 02. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/9891df1f-b429-461a-bc5e-b0520e25a120/, last accessed 09 Mar 2026
  14. Adhe, E.: Material: Black Painted Plaster Wall. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/61c2c84d-1701-4b19-9728-f0710bab1cce/, last accessed 09 Mar 2026
  15. Pambudi, R.R.: Material: Material Window Glass Cycle. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/4367fa4b-9633-48a2-862f-224fbf7b1730/, last accessed 09 Mar 2026
  16. Yang, R.: HDRi: Urban City Highsky Bluesky. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/139e9edb-ba7f-4848-b6c5-3b5c227ca805/, last accessed 09 Mar 2026 asset-gallery-detail/aa82da45-2e29-4e4d-9ec4-631977b4d29b/>
  17. Cosmo: Model: Suspension/Arch Bridge. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/5358ddd3-6dbf-4a1c-8731-4c6bdcfa8338/, last accessed 09 Mar 2026
  18. MapacheDRelease: Model: Crane Tower. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/eb5c137e-007e-485b-bbb9-675e76fcdac9/, last accessed 09 Mar 2026
  19. Mohammadi, S.: Material: Realistic Procedural Grass (Cycles/Eevee). BlenderKit, https://www.blenderkit.com/asset-gallery-detail/a465bd1e-a14d-442f-90b4-0bfb02dffa9f/, last accessed 09 Mar 2026
  20. Godoi, M.: Material: Procedural Concrete. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/6312d40c-7afb-49e3-b167-c772147ce40d/, last accessed 09 Mar 2026
  21. Russo 3D: Material: Procedural Brick. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/f5ab3780-1489-4c62-8050-b400bbd14e95/, last accessed 09 Mar 2026
  22. Middleton, J.: Material: Bricks Procedural Wall. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/fa2d62e6-fc17-4310-a0bf-b35eaefcfd3c/, last accessed 09 Mar 2026
  23. Middleton, J.: Material: Brick Wall Procedural. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/79f3e321-4923-45dd-8508-8082163db23d/, last accessed 09 Mar 2026
  24. FreePoly: HDRi: Over The Construction Site Yellow Mud. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/66411e15-4a1f-46cb-8e3a-0271e83dc8e4/, last accessed 09 Mar 2026
  25. FreePoly: HDRi: Lake Highsky Blue Sky. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/aa82da45-2e29-4e4d-9ec4-631977b4d29b/, last accessed 09 Mar 2026
  26. NK Productions: Material: Asphalt Road Procedural. BlenderKit, https://www.blenderkit.com/asset-gallery-detail/0f2ec127-987c-49ab-9478-7f777bdde2d3/, last accessed 09 Mar 2026

NB. [2] was used for it's drone model.
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