YCB-Ev SD: Synthetic event-vision dataset for 6DoF object pose estimation
Paper • 2511.11344 • Published • 2
Datasets:
Dataset Viewer
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: ValueError
Message: Invalid string class label ycbev_sd@5763b3e73c998fd8144971197ff36d6ff5029c19
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2386, in __iter__
example = _apply_feature_types_on_example(
example, self.features, token_per_repo_id=self.token_per_repo_id
)
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2303, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2178, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1483, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1158, in encode_example
example_data = self.str2int(example_data)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1095, in str2int
output = [self._strval2int(value) for value in values]
~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1116, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label ycbev_sd@5763b3e73c998fd8144971197ff36d6ff5029c19Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YCB-Ev SD: Synthetic event-vision dataset for 6DoF object pose estimation
Synthetic training data has become indispensable for frame-based 6D object pose estimation, yet an equivalent large-scale resource for event cameras is still missing in the YCB/BOP setting. We close this gap with YCB-Ev~SD, a dataset of 50,000 synthetic event sequences at standard-definition (SD) resolution. The sequences are generated from Physically Based Rendering (PBR) scenes of YCB-Video objects following the BOP challenge methodology, using simulated linear camera motion so that both foreground objects and background structure produce events. We adopt SD resolution (640x480px) because HD event sensors have been shown to be outperformed by SD sensors under fast motion due to increased temporal noise, and because SD matches the resolution of the existing BOP PBR images.
Building on this resource, we present the first systematic ablation of event-to-image representations for CNN-based 6D pose estimation. Time-surfaces with linear decay and dual-channel polarity encoding perform best, with polarity encoding contributing the largest individual gain: +8.2 and +6.4 percentage points for histograms and time-surfaces, respectively. Finally, we validate sim-to-real transfer on real event-camera captures acquired with an improved calibration pipeline (8.3ms synchronization accuracy).
https://arxiv.org/abs/2511.11344
Dataset Structure
- train_pbr / val_pbr / test_pbr - synthetic PBR event sequences (40K / 5K / 5K views)
- test - real event-camera captures (5 scenes, ~11,700 frames)
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