Datasets:
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Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
n_clips: int64
methods: list<item: string>
child 0, item: string
summary: struct<fov_bucket: struct<vace | 110-150: struct<ray_ae_median: double, ray_ae_p95: double, edge_ray (... 2168 chars omitted)
child 0, fov_bucket: struct<vace | 110-150: struct<ray_ae_median: double, ray_ae_p95: double, edge_ray_ae_median: double, (... 1006 chars omitted)
child 0, vace | 110-150: struct<ray_ae_median: double, ray_ae_p95: double, edge_ray_ae_median: double, edge_ray_ae_p95: doubl (... 159 chars omitted)
child 0, ray_ae_median: double
child 1, ray_ae_p95: double
child 2, edge_ray_ae_median: double
child 3, edge_ray_ae_p95: double
child 4, reproj_px_median: double
child 5, focal_rel_err: double
child 6, pp_err_px: double
child 7, fov_diagonal_err_deg: double
child 8, gravity_err_deg: double
child 9, family_correct: double
child 10, n: int64
child 1, vace | 70-110: struct<ray_ae_median: double, ray_ae_p95: double, edge_ray_ae_median: double, edge_ray_ae_p95: doubl (... 159 chars omitted)
child 0, ray_ae_median: double
child 1, ray_ae_p95: double
child 2, edge_ray_ae_median: double
child 3, edge_ray_ae_p95: double
child 4, reproj_px_median: double
child 5, focal_rel_err: double
child 6, pp_err_px: double
child 7, fov_diagonal_err_deg: double
child 8, gravity_err_deg: double
...
pp_err_norm: double
child 30, distortion_comparable: double
child 31, family_correct: double
child 32, gravity_err_deg: double
child 33, roll_mae_deg: double
child 34, pitch_mae_deg: double
child 35, uncertainty: double
child 36, solver_success: double
child 37, calibratability: double
child 38, cal_coverage: double
child 39, cal_observability: double
child 40, cal_fit: double
child 41, group_trajectory_family: string
child 42, group_source_type: string
child 43, group_resolution_limited: string
child 44, dist_alpha_err: double
child 45, dist_beta_err: double
child 46, dist_k1_err: double
child 47, dist_k2_err: double
child 48, dist_p1_err: double
child 49, dist_p2_err: double
child 50, dist_k3_err: double
checkpoint: string
loss_ratio: double
selected_checkpoint: string
backbone: string
resolution: list<item: int64>
child 0, item: int64
steps: int64
wall_seconds: double
overfit_clips: null
val: struct<val_ray_ae_median_deg: double, val_sem_deg: double, val_n_clips: double, best: double>
child 0, val_ray_ae_median_deg: double
child 1, val_sem_deg: double
child 2, val_n_clips: double
child 3, best: double
last_loss: double
parameters: struct<decoder: int64, frozen_backbone: int64, total: int64>
child 0, decoder: int64
child 1, frozen_backbone: int64
child 2, total: int64
first_loss: double
frames: int64
best_step: int64
val_best: double
to
{'backbone': Value('string'), 'frames': Value('int64'), 'resolution': List(Value('int64')), 'steps': Value('int64'), 'parameters': {'decoder': Value('int64'), 'frozen_backbone': Value('int64'), 'total': Value('int64')}, 'first_loss': Value('float64'), 'last_loss': Value('float64'), 'loss_ratio': Value('float64'), 'val': {'val_ray_ae_median_deg': Value('float64'), 'val_sem_deg': Value('float64'), 'val_n_clips': Value('float64'), 'best': Value('float64')}, 'val_best': Value('float64'), 'selected_checkpoint': Value('string'), 'best_step': Value('int64'), 'wall_seconds': Value('float64'), 'checkpoint': Value('string'), 'overfit_clips': Value('null')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
n_clips: int64
methods: list<item: string>
child 0, item: string
summary: struct<fov_bucket: struct<vace | 110-150: struct<ray_ae_median: double, ray_ae_p95: double, edge_ray (... 2168 chars omitted)
child 0, fov_bucket: struct<vace | 110-150: struct<ray_ae_median: double, ray_ae_p95: double, edge_ray_ae_median: double, (... 1006 chars omitted)
child 0, vace | 110-150: struct<ray_ae_median: double, ray_ae_p95: double, edge_ray_ae_median: double, edge_ray_ae_p95: doubl (... 159 chars omitted)
child 0, ray_ae_median: double
child 1, ray_ae_p95: double
child 2, edge_ray_ae_median: double
child 3, edge_ray_ae_p95: double
child 4, reproj_px_median: double
child 5, focal_rel_err: double
child 6, pp_err_px: double
child 7, fov_diagonal_err_deg: double
child 8, gravity_err_deg: double
child 9, family_correct: double
child 10, n: int64
child 1, vace | 70-110: struct<ray_ae_median: double, ray_ae_p95: double, edge_ray_ae_median: double, edge_ray_ae_p95: doubl (... 159 chars omitted)
child 0, ray_ae_median: double
child 1, ray_ae_p95: double
child 2, edge_ray_ae_median: double
child 3, edge_ray_ae_p95: double
child 4, reproj_px_median: double
child 5, focal_rel_err: double
child 6, pp_err_px: double
child 7, fov_diagonal_err_deg: double
child 8, gravity_err_deg: double
...
pp_err_norm: double
child 30, distortion_comparable: double
child 31, family_correct: double
child 32, gravity_err_deg: double
child 33, roll_mae_deg: double
child 34, pitch_mae_deg: double
child 35, uncertainty: double
child 36, solver_success: double
child 37, calibratability: double
child 38, cal_coverage: double
child 39, cal_observability: double
child 40, cal_fit: double
child 41, group_trajectory_family: string
child 42, group_source_type: string
child 43, group_resolution_limited: string
child 44, dist_alpha_err: double
child 45, dist_beta_err: double
child 46, dist_k1_err: double
child 47, dist_k2_err: double
child 48, dist_p1_err: double
child 49, dist_p2_err: double
child 50, dist_k3_err: double
checkpoint: string
loss_ratio: double
selected_checkpoint: string
backbone: string
resolution: list<item: int64>
child 0, item: int64
steps: int64
wall_seconds: double
overfit_clips: null
val: struct<val_ray_ae_median_deg: double, val_sem_deg: double, val_n_clips: double, best: double>
child 0, val_ray_ae_median_deg: double
child 1, val_sem_deg: double
child 2, val_n_clips: double
child 3, best: double
last_loss: double
parameters: struct<decoder: int64, frozen_backbone: int64, total: int64>
child 0, decoder: int64
child 1, frozen_backbone: int64
child 2, total: int64
first_loss: double
frames: int64
best_step: int64
val_best: double
to
{'backbone': Value('string'), 'frames': Value('int64'), 'resolution': List(Value('int64')), 'steps': Value('int64'), 'parameters': {'decoder': Value('int64'), 'frozen_backbone': Value('int64'), 'total': Value('int64')}, 'first_loss': Value('float64'), 'last_loss': Value('float64'), 'loss_ratio': Value('float64'), 'val': {'val_ray_ae_median_deg': Value('float64'), 'val_sem_deg': Value('float64'), 'val_n_clips': Value('float64'), 'best': Value('float64')}, 'val_best': Value('float64'), 'selected_checkpoint': Value('string'), 'best_step': Value('int64'), 'wall_seconds': Value('float64'), 'checkpoint': Value('string'), 'overfit_clips': Value('null')}
because column names don't match
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 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 1694, 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 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
backbone string | frames int64 | resolution list | steps int64 | parameters dict | first_loss float64 | last_loss float64 | loss_ratio float64 | val dict | val_best float64 | selected_checkpoint string | best_step int64 | wall_seconds float64 | checkpoint string | overfit_clips null |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
vace | 17 | [
240,
416
] | 20,000 | {
"decoder": 4902476,
"frozen_backbone": 2153972032,
"total": 4902476
} | 5.975214 | -2.465052 | -0.412546 | {
"val_ray_ae_median_deg": 10.77399586204964,
"val_sem_deg": 0.672927457721332,
"val_n_clips": 128,
"best": 10.77399586204964
} | 10.773996 | final | 20,000 | 34,993.5 | /workspace/sglang/vidicalib/runs/controlled_repaired_20260913/base_raw_vace/decoder.pt | null |
vace | 17 | [
240,
416
] | 20,000 | {
"decoder": 4902476,
"frozen_backbone": 2153972032,
"total": 4902476
} | 3.315567 | -2.431293 | -0.733296 | {
"val_ray_ae_median_deg": 11.194855373403177,
"val_sem_deg": 0.6804071053752561,
"val_n_clips": 128,
"best": 11.194855373403177
} | 11.194855 | final | 20,000 | 40,881.1 | /workspace/sglang/vidicalib/runs/controlled_repaired_20260913/base_stage1a/decoder.pt | null |
vace | 17 | [
240,
416
] | 20,000 | {
"decoder": 4902476,
"frozen_backbone": 2153972032,
"total": 4902476
} | 3.992294 | -1.804413 | -0.451974 | {
"val_ray_ae_median_deg": 10.428065535260759,
"val_sem_deg": 0.6249596357633131,
"val_n_clips": 128,
"best": 10.428065535260759
} | 10.428066 | final | 20,000 | 40,860.3 | /workspace/sglang/vidicalib/runs/controlled_repaired_20260913/base_stage1a1b/decoder.pt | null |
ViDiCalib — wide-angle camera calibration from video
Synthetic video clips rendered from equirectangular panoramas through known wide-angle camera models, with exact per-pixel ground truth: the ray field, gravity, latitude/up fields and validity masks used to produce the pixels are returned by the renderer, not estimated from them.
This release contains the labels, manifests, out-of-distribution test set and measured results. The rendered RGB for the 49,434-clip training split is 359 GB and is not included here; it is reproducible from the manifests and the public panoramas (see Reproducing the renders).
What is here
| path | contents |
|---|---|
manifests/paper.manifest.jsonl |
49,434 clips, 4,004,154 frames, 749 panoramas |
manifests/ood.manifest.jsonl |
600 clips from measured lens profiles |
manifests/paper.qc_report.json |
QC gate result for the training split |
labels/paper_dense_gt.tar |
exact dense ground truth, one .npz per clip |
labels/paper_poses.tar |
camera trajectories |
labels/ood_*.tar |
the same for the OOD split, plus its shared ray grids |
ood/ood_videos.tar |
the OOD clips' RGB (3.7 GB) — usable as-is |
lens_profiles/ |
6,474 Lensfun/Optiland profiles, 1,436 families, with the train/test family split |
results/ |
the controlled three-arm experiment: per-clip metrics and summaries |
docs/ |
conventions, data provenance, results, model card, repair report |
Conventions
Camera frame is x right, y down, z forward. Pixel centres are at integers
— AnyCalib and GeoCalib put them at integer + 0.5, so cx_ours = cx_theirs − 0.5.
Gravity is g(roll, pitch) = (sin r cos p, cos r cos p, −sin p); GeoCalib's is
the negation of this. Getting either wrong produces plausible-looking numbers
that are wrong by a constant, so docs/CONVENTIONS.md states them explicitly.
Splits
Partitioned by panorama and by source group, never by clip, so no panorama contributes to two splits: train 44,220 / val 2,574 / test 2,640.
The OOD split is separate and stronger: its lenses come from 127 lens families the training split never contains, and its ground truth is each profile's own ray grid rather than a parametric fit — 88% of held-out profiles have no converged parametric fit, and those that do are off by up to 1°.
Imaging conditions
clean 0.40, sensor noise 0.12, night 0.12, low texture 0.12, motion blur 0.08, dynamic occlusion 0.16. Motion blur is a genuine exposure integral, so it smears along the actual motion. Occluders are accepted only under a recoverability invariant — every pixel visible in at least one frame — because a pixel hidden in every frame is recoverable by no method and only adds label noise. Measured worst case over the whole split: 0.001.
Results included
A controlled three-arm comparison of generative geometry pre-training, with every variable fixed except the pre-trained branch:
| arm | test ray AE (2,640 clips) | OOD ray AE (600 clips) |
|---|---|---|
| no geometry pre-training | 10.312 | 9.724 |
| Stage-1a | 10.431 | 10.879 |
| Stage-1a + 1b | 10.128 | 10.149 |
Paired bootstrap against the no-pre-training arm: Stage-1a is significantly worse (test +0.223°, CI [+0.133, +0.328]; OOD +0.965°, CI [+0.744, +1.170]); Stage-1a+1b is not distinguishable (test −0.087°, CI [−0.211, +0.039]; OOD +0.199°, CI [−0.212, +0.520]).
The pre-training does learn geometry — free-running generation improves ray angular error from 101° to 19° and temporal consistency from 60.9° to 0.68° — but that did not transfer to downstream calibration accuracy. For reference, AnyCalib scores 4.804° on the same test split.
Reproducing the renders
The 359 GB of training RGB is deterministic given the manifests and the public OpenPano panoramas. Each manifest record carries the camera model and parameters, the trajectory, the encoder settings, the imaging condition and a SHA-256 of the produced video, so a re-render can be checked byte for byte.
Limitations
Translation is ignored (a panorama is at infinity), so parallax is absent. Occlusion is procedural, not real objects. The Laval Indoor HDR panoramas the OpenPano split lists also name require a manual application and are absent — 2,162 of 2,908 listed panoramas are missing, so the set is outdoor- and architecture-heavy relative to what those lists describe.
See ATTRIBUTION.md for licensing: the panoramas and the lens database carry
their own terms and those flow through to these derived clips.
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