The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label Transparent_finetune_dataset@04c53156e3c42b9f833001d6003e5e881140461f
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 "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2092, in _iter_arrow
pa_table = cast_table_to_features(pa_table, self.features)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2197, in cast_table_to_features
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1995, in cast_array_to_feature
return feature.cast_storage(array)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1169, in cast_storage
[self._strval2int(label) if label is not None else None for label in storage.to_pylist()]
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1098, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label Transparent_finetune_dataset@04c53156e3c42b9f833001d6003e5e881140461fNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Object Pose Estimation Using Implicit Representation for Transparent Objects
This dataset consists of raw rendered Physically Based Rendering (PBR) data and 3D mesh assets designed for training and fine-tuning pose-estimation models, specifically adapting Megapose for transparent objects. Created by Varun Burde in November 2024 and prepared for distribution in February 2026, the data provides a comprehensive resource for implicit representation research.
Visualization
Below is a sample visualization showing the RGB render, Depth map, and Visibility Mask (stacked sequentially):
Dataset Structure
The dataset is organized into zipped archives for easier accessibility. You will need to unzip these files to access the training data.
Archives
meshes_and_meta.zip: Contains object meshes (.ply) and global camera parameters.train_pbr_xxx_xxx.zip: Split archives containing the rendered training sequences.
Internal File Structure (after unzipping)
The data follows the BOP (Benchmark for 6D Object Pose Estimation) directory structure:
dataset/
├── camera.json # Global camera intrinsics/extrinsics
├── meshes/
│ ├── models_info.json # Metadata about the 3D models
│ ├── obj_000000.ply # 3D Mesh file for object 0
│ ├── obj_000001.ply # 3D Mesh file for object 1
│ └── ...
└── train_pbr/
├── 000000/ # Scene ID
│ ├── scene_camera.json # Camera parameters for each frame in this scene
│ ├── scene_gt.json # Ground truth 6D poses
│ ├── scene_gt_info.json # Bounding box and visibility info
│ ├── rgb/
│ │ ├── 000000.png # RGB Image
│ │ └── ...
│ ├── depth/
│ │ ├── 000000.png # Depth Map
│ │ └── ...
│ └── mask_visib/
│ ├── 000000_000000.png # Visibility mask for object instance 0
│ └── ...
├── 000001/
└── ...
This work is associated with the research published in Object Pose Estimation Using Implicit Representation for Transparent Objects, available at SpringerView.
In creating this dataset, we utilized the BOP Toolkit for standardized formatting and BlenderProc for the underlying synthetic data generation. This work was supported by the European Union under the project Robotics and advanced industrial production (reg. no. CZ.02.01.01/00/22_008/0004590).
The dataset is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Users are free to share and adapt the material provided they give appropriate credit to the authors and the associated publication.
If you find this dataset useful, please cite it using:
@InProceedings{10.1007/978-3-031-91569-7_15,
author="Burde, Varun and Moroz, Artem and Zeman, V{\'i}t and Burget, Pavel",
editor="Del Bue, Alessio and Canton, Cristian and Pont-Tuset, Jordi and Tommasi, Tatiana",
title="Object Pose Estimation Using Implicit Representation for Transparent Objects",
booktitle="Computer Vision -- ECCV 2024 Workshops",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="226--247",
isbn="978-3-031-91569-7"
}
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