Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
camera_model: string
image_width: int64
image_height: int64
intrinsics: struct<fx: double, fy: double, cx: double, cy: double>
distortion: struct<k1: double, k2: double, p1: double, p2: double, k3: double>
vs
id_to_name: struct<1: string, 2: string, 3: string, 4: string, 5: string, 6: string, 7: string, 8: string, 9: string, 10: string, 11: string, 12: string, 13: string, 14: string, 15: string, 16: string, 17: string, 18: string, 19: string, 20: string, 21: string, 22: string, 23: string, 24: string, 25: string, 26: string, 27: string, 28: string, 29: string, 30: string, 31: string, 32: string, 33: string, 34: string, 35: string, 36: string, 37: string, 38: string, 39: string, 40: string>
name_to_id: struct<wall: int64, floor: int64, cabinet: int64, bed: int64, chair: int64, sofa: int64, table: int64, door: int64, window: int64, bookshelf: int64, picture: int64, counter: int64, blinds: int64, desk: int64, shelves: int64, curtain: int64, dresser: int64, pillow: int64, mirror: int64, floor mat: int64, clothes: int64, ceiling: int64, books: int64, refridgerator: int64, television: int64, paper: int64, towel: int64, shower curtain: int64, box: int64, whiteboard: int64, person: int64, night stand: int64, toilet: int64, sink: int64, lamp: int64, bathtub: int64, bag: int64, otherstructure: int64, otherfurniture: int64, otherprop: int64>
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 563, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
camera_model: string
image_width: int64
image_height: int64
intrinsics: struct<fx: double, fy: double, cx: double, cy: double>
distortion: struct<k1: double, k2: double, p1: double, p2: double, k3: double>
vs
id_to_name: struct<1: string, 2: string, 3: string, 4: string, 5: string, 6: string, 7: string, 8: string, 9: string, 10: string, 11: string, 12: string, 13: string, 14: string, 15: string, 16: string, 17: string, 18: string, 19: string, 20: string, 21: string, 22: string, 23: string, 24: string, 25: string, 26: string, 27: string, 28: string, 29: string, 30: string, 31: string, 32: string, 33: string, 34: string, 35: string, 36: string, 37: string, 38: string, 39: string, 40: string>
name_to_id: struct<wall: int64, floor: int64, cabinet: int64, bed: int64, chair: int64, sofa: int64, table: int64, door: int64, window: int64, bookshelf: int64, picture: int64, counter: int64, blinds: int64, desk: int64, shelves: int64, curtain: int64, dresser: int64, pillow: int64, mirror: int64, floor mat: int64, clothes: int64, ceiling: int64, books: int64, refridgerator: int64, television: int64, paper: int64, towel: int64, shower curtain: int64, box: int64, whiteboard: int64, person: int64, night stand: int64, toilet: int64, sink: int64, lamp: int64, bathtub: int64, bag: int64, otherstructure: int64, otherfurniture: int64, otherprop: int64>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.
img_pointV2 is available 🎉🎉🎉🥳🥳😀😀
This dataset is a collection of 3D point clouds generated from the jagennath-hari/nyuv2dataset.
img_pointV2 is the second version of the RAY-AUTRA-TECHNOLOGY/img_pointV dataset. It is a spatialized version of the NYU Depth V2 dataset, transforming classic indoor images into high-fidelity 3D point clouds (.ply files).
The main objective is to provide clean, ready-to-use 3D scenes for training 3D vision models, eliminating the need for users to manually handle RGB-D to point cloud conversion.
Dataset Highlights
- Point Clouds (.ply): Complete 3D scenes featuring both geometry ($X, Y, Z$) and color ($R, G, B$).
- Metric Precision: Every point is accurately positioned in meters, strictly following the real-world Kinect camera intrinsic parameters.
- Cleaned & Uniformed: Clouds have been filtered to remove capture noise and voxelized with a 1 cm density (voxel size: $0.01$).
- Integrated Labels: Metadata preserves all original semantic and instance segmentation information.
File Structure
| File/Folder | Description |
|---|---|
data/ |
Directory containing the .ply files. |
metadata.arrow |
Central index linking IDs, filenames, and point counts (Train/Val/Test splits). |
camera_params.json |
Optical parameters (intrinsics) used for the 3D reconstruction. |
class_names.json |
Dictionary of semantic classes (e.g., chair, wall, table). |
config.yaml |
Dataset configuration (license, format, normalization). |
IMPORTANT: These files are fully compatible with major 3D libraries such as Open3D, PyTorch Geometric, and PointNet++.
RAY AUTRA TECHNOLOGY 2025
- Downloads last month
- -