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princeton365

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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
__key__: string
__url__: string
json: struct<category: string, experiment_id: string, split: string>
  child 0, category: string
  child 1, experiment_id: string
  child 2, split: string
mp4: binary
user_camera_mtx.npy: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
user_camera_dist.npy: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
relative_transform.npy: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
gt_trajectory.txt: string
gt_view.mp4: binary
gt_camera_mtx.npy: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
gt_intrinsics_rotation_360.json: struct<rotation: struct<fov: double, pan: double, roll: double, tilt: double>>
  child 0, rotation: struct<fov: double, pan: double, roll: double, tilt: double>
      child 0, fov: double
      child 1, pan: double
      child 2, roll: double
      child 3, tilt: double
gt_camera_dist.npy: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
to
{'__key__': Value('string'), '__url__': Value('string'), 'json': {'category': Value('string'), 'experiment_id': Value('string'), 'split': Value('string')}, 'mp4': Value('binary'), 'user_camera_mtx.npy': List(List(Value('float64'))), 'user_camera_dist.npy': List(List(Value('float64')))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2361, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1914, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2192, in cast_table_to_features
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              __key__: string
              __url__: string
              json: struct<category: string, experiment_id: string, split: string>
                child 0, category: string
                child 1, experiment_id: string
                child 2, split: string
              mp4: binary
              user_camera_mtx.npy: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              user_camera_dist.npy: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              relative_transform.npy: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              gt_trajectory.txt: string
              gt_view.mp4: binary
              gt_camera_mtx.npy: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              gt_intrinsics_rotation_360.json: struct<rotation: struct<fov: double, pan: double, roll: double, tilt: double>>
                child 0, rotation: struct<fov: double, pan: double, roll: double, tilt: double>
                    child 0, fov: double
                    child 1, pan: double
                    child 2, roll: double
                    child 3, tilt: double
              gt_camera_dist.npy: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              to
              {'__key__': Value('string'), '__url__': Value('string'), 'json': {'category': Value('string'), 'experiment_id': Value('string'), 'split': Value('string')}, 'mp4': Value('binary'), 'user_camera_mtx.npy': List(List(Value('float64'))), 'user_camera_dist.npy': List(List(Value('float64')))}
              because column names don't match

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Princeton365 Dataset

This is a webdataset version of the Princeton365 dataset. Please find the download instructions in the official repository

Dataset Structure

Each sequence is stored in a numbered shard (e.g., 000000.tar) with flat file structure and shared prefixes:

All files share the prefix and are stored flat:

  • sequence_id.json: Sequence metadata (experiment_id, category, split)
  • sequence_id.mp4: User view video
  • sequence_id.user_camera_dist.npy: User view camera distortion parameters
  • sequence_id.user_camera_mtx.npy: User view camera intrinsic matrix
  • sequence_id.gt_camera_dist.npy: GT view camera distortion parameters (validation only)
  • sequence_id.gt_camera_mtx.npy: GT view camera intrinsic matrix (validation only)
  • sequence_id.relative_transform.npy: Relative transformation from user to GT view
  • sequence_id.gt_trajectory.txt: Ground truth camera trajectory (validation only)
  • sequence_id.gt_view.mp4: Ground truth view video (validation only)
  • sequence_id.imu.csv: IMU data
  • sequence_id.left_stereo.mp4: Left stereo camera images (packed as MP4)
  • sequence_id.left_stereo_mapping.json: Frame-to-timestamp mapping for left stereo images
  • sequence_id.right_stereo.mp4: Right stereo camera images (packed as MP4)
  • sequence_id.right_stereo_mapping.json: Frame-to-timestamp mapping for right stereo images
  • sequence_id.depth_frame_*.h5: Depth data files for evaluation (validation only)
  • sequence_id.confidence_frame_*.h5: Depth confidence files for evaluation (validation only)
  • JSON metadata includes stereo_effective_fps: Effective stereo camera FPS

Citation 

@misc{princeton365,
      title={Princeton365: A Diverse Dataset with Accurate Camera Pose}, 
      author={Karhan Kayan and Stamatis Alexandropoulos and Rishabh Jain and Yiming Zuo and Erich Liang and Jia Deng},
      year={2025},
      eprint={2506.09035},
    archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.09035}, 
}
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