Datasets:

chanwook
/

actioncam-vio

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 10 new columns ({'purpose', 'width', 'scene', 'camera', 'height', 'fps', 'duration_s', 'size_mb', 'codec', 'file'}) and 12 missing columns ({'is_keyframe', 'z', 'is_lost', 'q_z', 'q_x', 'q_w', 'timestamp', 'frame_idx', 'state', 'x', 'y', 'q_y'}).

This happened while the csv dataset builder was generating data using

hf://datasets/chanwook/actioncam-vio/videos.csv (at revision 610e3c083c7453b87289d5629f1235e86849c6d2), ['hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/acepro2/VID_520/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/acepro2/VID_521/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/acepro2/VID_525/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/acepro2/VID_526/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/acepro2/VID_527/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/acepro2/VID_528/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/hero7black/GX014219/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/hero7black/GX014220/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/hero7black/GX014221/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/hero7black/GX014222/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/videos.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              camera: string
              file: string
              purpose: string
              codec: string
              width: int64
              height: int64
              fps: double
              duration_s: double
              size_mb: int64
              scene: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1387
              to
              {'frame_idx': Value('int64'), 'timestamp': Value('float64'), 'state': Value('int64'), 'is_lost': Value('bool'), 'is_keyframe': Value('bool'), 'x': Value('float64'), 'y': Value('float64'), 'z': Value('float64'), 'q_x': Value('float64'), 'q_y': Value('float64'), 'q_z': Value('float64'), 'q_w': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 10 new columns ({'purpose', 'width', 'scene', 'camera', 'height', 'fps', 'duration_s', 'size_mb', 'codec', 'file'}) and 12 missing columns ({'is_keyframe', 'z', 'is_lost', 'q_z', 'q_x', 'q_w', 'timestamp', 'frame_idx', 'state', 'x', 'y', 'q_y'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/chanwook/actioncam-vio/videos.csv (at revision 610e3c083c7453b87289d5629f1235e86849c6d2), ['hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/acepro2/VID_520/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/acepro2/VID_521/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/acepro2/VID_525/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/acepro2/VID_526/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/acepro2/VID_527/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/acepro2/VID_528/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/hero7black/GX014219/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/hero7black/GX014220/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/hero7black/GX014221/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/outputs/hero7black/GX014222/camera_trajectory.csv', 'hf://datasets/chanwook/actioncam-vio@610e3c083c7453b87289d5629f1235e86849c6d2/videos.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

frame_idx int64	timestamp float64	state int64	is_lost bool	is_keyframe bool	x float64	y float64	z float64	q_x float64	q_y float64	q_z float64	q_w float64
0	0	1	true	false	0	0	0	0	0	0	0
1	0	1	true	false	0	0	0	0	0	0	0
2	0	1	true	false	0	0	0	0	0	0	0
3	0	1	true	false	0	0	0	0	0	0	0
4	0.133467	2	false	true	-0.058322	-0.004474	0.050118	0.012051	0.000448	0.008191	0.999894
5	0.166833	2	false	false	-6.309465	-0.918049	4.996404	0.017011	0.002401	0.010678	0.999795
6	0.2002	2	false	false	-7.401492	-0.753637	5.767027	0.017656	-0.00017	0.013118	0.999758
7	0.233567	2	false	false	-8.142653	-1.27919	6.58608	0.013062	-0.003541	0.011957	0.999837
8	0.266933	2	false	false	-8.697871	-1.410318	8.111019	0.011921	0.007584	0.009156	0.999858
9	0.3003	2	false	false	-9.519197	-1.670972	9.451443	0.01628	0.01973	0.007844	0.999642
10	0.333667	2	false	false	-10.481191	-1.828992	10.389161	0.019339	0.02914	0.009049	0.999347
11	0.367033	2	false	false	-11.036363	-2.336069	11.394747	0.014595	0.033296	0.011182	0.999276
12	0.4004	2	false	true	-0.136068	-0.022167	0.167888	0.012381	0.035415	0.011726	0.999227
13	0.433767	2	false	false	-11.93492	-2.573566	14.340114	0.007157	0.039861	0.01113	0.999118
14	0.467133	2	false	false	-12.49137	-2.680929	15.793525	0.00617	0.038083	0.011669	0.999187
15	0.5005	2	false	false	-4.3662	-1.295937	7.325952	0.002366	0.039394	0.015961	0.999093
16	0.533867	2	false	false	-4.510297	-1.497124	8.254473	0.000432	0.046349	0.018202	0.998759
17	0.567233	2	false	false	-4.700187	-1.434195	9.226256	-0.000051	0.048068	0.019361	0.998656
18	0.6006	2	false	false	-4.803164	-1.426867	10.416758	0.005139	0.045536	0.017357	0.998799
19	0.633967	2	false	false	-4.916533	-1.452445	11.392078	0.010962	0.04035	0.015019	0.999013
20	0.667333	2	false	true	-0.15894	-0.027804	0.34971	0.007619	0.034838	0.015313	0.999247
21	0.7007	2	false	false	-0.18321	-0.047122	1.712443	0.003775	0.032713	0.015203	0.999342
22	0.734067	2	false	false	-5.009965	-1.572022	14.914291	0.004335	0.034869	0.012142	0.999309
23	0.767433	2	false	false	-5.060803	-1.657069	16.122572	0.00652	0.032016	0.00905	0.999425
24	0.8008	2	false	false	-0.309601	-0.121281	4.310194	0.012335	0.022913	0.007866	0.99963
25	0.834167	2	false	false	-0.264067	-0.347339	5.276361	0.013307	0.016398	0.010596	0.999721
26	0.867533	2	false	false	-0.318212	-0.435725	6.250545	0.015206	0.014665	0.013622	0.999684
27	0.9009	2	false	false	-0.307329	-0.646551	7.332968	0.017482	0.013143	0.013963	0.999663
28	0.934267	2	false	true	-0.157189	-0.051563	0.588771	0.025779	0.013524	0.010039	0.999526
29	0.967633	2	false	false	-0.326965	-0.216956	1.509654	0.030204	0.017269	0.007443	0.999367
30	1.001	2	false	false	-0.600682	-0.845608	10.366067	0.025739	0.01416	0.012627	0.999489
31	1.034367	2	false	false	-0.453577	-0.309488	2.81144	0.019625	0.006792	0.016052	0.999656
32	1.067733	2	false	false	-0.455267	-0.279314	3.502436	0.02184	0.007333	0.015934	0.999608
33	1.1011	2	false	false	-0.628314	-0.270426	4.266539	0.019595	0.004401	0.015317	0.999681
34	1.134467	2	false	false	-0.67288	-0.306069	5.149984	0.021176	-0.005691	0.008816	0.999721
35	1.167833	2	false	false	-0.74724	-0.356782	5.96475	0.035503	-0.00978	-0.003473	0.999316
36	1.2012	2	false	true	-0.182759	-0.056492	0.849128	0.040592	-0.004925	-0.008811	0.999125
37	1.234567	2	false	false	-0.268001	-0.006281	1.675341	0.030195	0.00093	-0.004852	0.999532
38	1.267933	2	false	false	-1.04813	-0.239867	8.47853	0.030227	0.009075	-0.006126	0.999483
39	1.3013	2	false	false	-0.847719	-0.118735	6.474802	0.034843	0.01467	-0.009249	0.999242
40	1.334667	2	false	false	-0.516783	-0.074979	3.165321	0.026854	0.018307	-0.003092	0.999467
41	1.368033	2	false	false	-0.536199	-0.121465	3.760868	0.017797	0.025049	0.00383	0.999521
42	1.4014	2	false	false	-0.598898	-0.19546	4.387116	0.014467	0.03187	0.003595	0.999381
43	1.434767	2	false	false	-0.725242	-0.256914	4.941589	0.013426	0.036684	0.002985	0.999232
44	1.468133	2	false	true	-0.220346	-0.07418	1.139806	0.0099	0.039317	0.004726	0.999167
45	1.5015	2	false	false	-0.172962	-0.160543	1.679589	0.008874	0.041241	0.003217	0.999105
46	1.534867	2	false	false	-0.252242	-0.181869	2.283274	0.009652	0.04628	0.002641	0.998878
47	1.568233	2	false	false	-0.312445	-0.285137	2.848795	0.007265	0.051481	0.006541	0.998626
48	1.6016	2	false	false	-0.272989	-0.199877	2.526933	0.009653	0.051744	0.010338	0.99856
49	1.634967	2	false	false	-0.250921	-0.247371	2.858927	0.013012	0.053349	0.014498	0.998386
50	1.668333	2	false	false	-0.254852	-0.249121	3.19191	0.01352	0.054296	0.020448	0.998224
51	1.7017	2	false	false	-0.224217	-0.231789	3.560913	0.015761	0.05203	0.023162	0.998253
52	1.735067	2	false	true	-0.218583	-0.08774	1.428352	0.019315	0.053839	0.022993	0.998098
53	1.768433	2	false	false	-0.196419	-0.064332	1.825478	0.022999	0.05276	0.021822	0.998104
54	1.8018	2	false	false	-0.194966	-0.048944	2.203243	0.025295	0.048296	0.021041	0.998291
55	1.835167	2	false	false	-0.139073	-0.022075	2.598332	0.025486	0.045376	0.021792	0.998407
56	1.868533	2	false	false	-0.20353	-0.074998	1.662166	0.024832	0.041277	0.020807	0.998622
57	1.9019	2	false	false	-0.198623	-0.075319	1.728265	0.026628	0.034969	0.015645	0.998911
58	1.935267	2	false	false	-0.191054	-0.07876	1.791421	0.025064	0.031223	0.011941	0.999127
59	1.968633	2	false	false	-0.185016	-0.08009	1.857552	0.018468	0.038426	0.011883	0.99902
60	2.002	2	false	true	-0.189881	-0.088725	1.796359	0.008941	0.043562	0.013428	0.998921
61	2.035367	2	false	false	-0.187971	-0.097355	1.864862	0.005391	0.046484	0.013941	0.998807
62	2.068733	2	false	false	-0.185238	-0.099149	1.92931	0.007554	0.043593	0.013987	0.998923
63	2.1021	2	false	false	-0.18431	-0.100142	1.992026	0.003797	0.041803	0.015735	0.998995
64	2.135467	2	false	false	-0.183686	-0.09251	1.993564	0.001653	0.045398	0.016119	0.998838
65	2.168833	2	false	false	-0.180562	-0.092166	2.040164	0.001029	0.048605	0.012788	0.998736
66	2.2022	2	false	false	-0.182939	-0.090847	2.087168	0.002402	0.047785	0.00883	0.998816
67	2.235567	2	false	false	-0.184228	-0.087656	2.136009	0.002553	0.045416	0.008559	0.998928
68	2.268933	2	false	true	-0.187245	-0.092624	2.181199	0.001356	0.049773	0.007754	0.99873
69	2.3023	2	false	false	-0.185753	-0.092024	2.228225	-0.002277	0.05609	0.00536	0.998409
70	2.335667	2	false	false	-0.184095	-0.090881	2.28246	0.003121	0.060043	-0.005335	0.998177
71	2.369033	2	false	false	-0.190442	-0.09118	2.327777	0.00871	0.065283	-0.014852	0.997718
72	2.4024	2	false	false	-0.198315	-0.092807	2.374963	0.007217	0.06789	-0.015582	0.997545
73	2.435767	2	false	false	-0.196393	-0.10415	2.403331	-0.002725	0.06524	-0.012317	0.99779
74	2.469133	2	false	false	-0.19654	-0.109297	2.447379	-0.003258	0.062393	-0.016098	0.997917
75	2.5025	2	false	false	-0.19618	-0.113988	2.491559	-0.010677	0.062115	-0.0112	0.997949
76	2.535867	2	false	true	-0.195091	-0.116705	2.561225	-0.020474	0.062779	-0.005375	0.997803
77	2.569233	2	false	false	-0.19223	-0.118724	2.607857	-0.017922	0.062958	-0.007948	0.997824
78	2.6026	2	false	false	-0.1918	-0.121774	2.627936	-0.011013	0.063111	-0.009536	0.9979
79	2.635967	2	false	false	-0.189825	-0.119921	2.698717	-0.008148	0.063033	-0.002587	0.997975
80	2.669333	2	false	true	-0.183556	-0.12334	2.75299	-0.005406	0.062871	0.004324	0.997998
81	2.7027	2	false	false	-0.179412	-0.121806	2.801209	0.00194	0.06419	0.00554	0.99792
82	2.736067	2	false	false	-0.175332	-0.120658	2.849055	0.004509	0.067453	0.010606	0.997656
83	2.769433	2	false	false	-0.170444	-0.121033	2.897965	0.007118	0.065827	0.016897	0.997663
84	2.8028	2	false	true	-0.162199	-0.123977	2.955973	0.019139	0.059549	0.014821	0.997932
85	2.836167	2	false	false	-0.158224	-0.127122	3.001648	0.025973	0.053743	0.015051	0.998103
86	2.869533	2	false	false	-0.153966	-0.131519	3.048444	0.02025	0.053416	0.021208	0.998142
87	2.9029	2	false	false	-0.149763	-0.136176	3.095949	0.012104	0.059063	0.021475	0.99795
88	2.936267	2	false	true	-0.144027	-0.142226	3.162754	0.007239	0.064741	0.015949	0.997748
89	2.969633	2	false	false	-0.141221	-0.146981	3.209427	0.007771	0.066709	0.013649	0.997649
90	3.003	2	false	false	-0.138717	-0.150803	3.255089	0.007994	0.06288	0.014186	0.997888
91	3.036367	2	false	false	-0.136493	-0.153503	3.303326	0.004872	0.062039	0.016468	0.997926
92	3.069733	2	false	false	-0.134101	-0.153872	3.349735	0.006358	0.068237	0.013746	0.997554
93	3.1031	2	false	true	-0.132452	-0.152817	3.419528	0.008591	0.069759	0.009252	0.997484
94	3.136467	2	false	false	-0.132364	-0.150063	3.469586	0.011571	0.068856	0.006736	0.997537
95	3.169833	2	false	false	-0.133543	-0.14808	3.51774	0.015429	0.070336	0.007284	0.997377
96	3.2032	2	false	false	-0.13426	-0.146948	3.565789	0.017278	0.07526	0.00666	0.996992
97	3.236567	2	false	false	-0.135573	-0.146923	3.612674	0.020166	0.07415	0.005217	0.99703
98	3.269933	2	false	true	-0.136651	-0.149088	3.674898	0.02058	0.073681	0.004502	0.997059
99	3.3033	2	false	false	-0.137245	-0.154202	3.721131	0.019839	0.077299	0.009944	0.996761

End of preview.

actioncam-vio 데이터셋

actioncam-vio 파이프라인(UMI 스타일 액션캠 VIO)의 원본 영상 + SLAM 산출물입니다. 액션캠 하나로 촬영한 MP4에서 RGB + IMU(내장 텔레메트리)를 추출해 캘리브레이션·ORB-SLAM3 mono-inertial까지 재현할 수 있습니다.

카메라

카메라	영상	IMU	FOV (H/V/D)
GoPro HERO7 Black	2704×2028 @59.94, H.264	GPMF 197.7 Hz	120.5°/93.7°/146.6°
Insta360 Ace Pro 2	2688×2016 @59.94, HEVC	트레일러 994 Hz	130.1°/104.0°/152.3°

인카메라 안정화 OFF 상태로 촬영 (VIO 기하 보존)
IMU는 영상 파일 안에 포함되어 있음 (별도 파일 없음 — 파이프라인이 추출)
ChArUco 보드: 10×8칸, 정사각형 0.023 m, DICT_ARUCO_ORIGINAL legacy 패턴

구성

hero7black/   GoPro 원본 5편 (보드 1, SLAM 4)         ~5.3 GB
acepro2/      Insta360 원본 6편 (보드 2, SLAM 4 — 주간/야간/지하주차장)  ~5.0 GB
              + 웹캠(UVC) 모드 보드 영상 2편 (1080p30 MJPEG — VLA 롤아웃 기하 캘리브레이션용)
outputs/      ORB-SLAM3 산출물 (궤적 csv/tum, 통계, 맵 점군 ply, rerun rrd)
              — 코드 저장소 커밋 02d695a 기준 산출물 (ORB-SLAM3 비결정성 때문에
                재실행 결과와 수 % 차이 가능; 문서의 수치는 이 산출물 기준)
videos.csv    영상별 메타데이터 (해상도/길이/용도/장면)

영상별 상세는 videos.csv 참조. 대표 결과:

근거리 ATE(ChArUco mm급 기준 대비): Ace Pro 2 1.23 cm / HERO7 1.88 cm (rigid)
저조도(지하주차장/야간 공원): 99.8% / 98.8% 추적 — VID_528(주간)과 VID_521(야간)은 같은 장소의 주/야간 페어 벤치마크

사용법

# 코드 저장소와 같은 배치로 다운로드
hf download chanwook/actioncam-vio --repo-type dataset --local-dir data
# 이후 github.com/redEddie/actioncam-vio 의 README 명령이 그대로 동작

주의

공공장소에서 촬영된 영상으로 행인이 포함될 수 있습니다. 연구 목적으로만 사용하고, 재배포 시 해당 지역 개인정보 규정을 준수하세요.

Downloads last month: 5

Total file size:

11.6 GB