Transparent Finetune Dataset
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 distr...
| Entity Passport | |
| Registry ID | hf-dataset--varunburde--transparent_finetune_dataset |
| Provider | huggingface |
Cite this dataset
Academic & Research Attribution
@misc{hf_dataset__varunburde__transparent_finetune_dataset,
author = {varunburde},
title = {Transparent Finetune Dataset Dataset},
year = {2026},
howpublished = {\url{https://huggingface.co/datasets/varunburde/Transparent_finetune_dataset}},
note = {Accessed via Free2AITools Knowledge Fortress}
} π¬Technical Deep Dive
Full Specifications [+]βΎ
π¬ Why this score?
The Nexus Index for Transparent Finetune Dataset aggregates Popularity (P:0), Velocity (V:0), and Credibility (C:0). The Utility score (U:0) represents deployment readiness, context efficiency, and structural reliability within the Nexus ecosystem.
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Dataset Specification
license: cc-by-4.0
task_categories:
- object-detection
- robotics
tags: - 6d-pose-estimation
- transparency
- megapose
- bop
- synthetic
pretty_name: Transparent Object Pose Estimation
size_categories: - 100K<n<1M
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/
βββ camera.json # Global camera intrinsics/extrinsics
βββ meshes/
β βββ models_info.json # Metadata about the 3D models
β βββ obj_000000.ply # 3D Mesh file for object 0
β βββ obj_000001.ply # 3D Mesh file for object 1
β βββ ...
βββ train_pbr/
βββ 000000/ # Scene ID
β βββ scene_camera.json # Camera parameters for each frame in this scene
β βββ scene_gt.json # Ground truth 6D poses
β βββ scene_gt_info.json # Bounding box and visibility info
β βββ rgb/
β β βββ 000000.png # RGB Image
β β βββ ...
β βββ depth/
β β βββ 000000.png # Depth Map
β β βββ ...
β βββ mask_visib/
β βββ 000000_000000.png # Visibility mask for object instance 0
β βββ ...
βββ 000001/
βββ ...
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"
}
Social Proof
AI Summary: Based on Hugging Face metadata. Not a recommendation.
π‘οΈ Dataset Transparency Report
Verified data manifest for traceability and transparency.
π Identity & Source
- id
- hf-dataset--varunburde--transparent_finetune_dataset
- source
- huggingface
- author
- varunburde
- tags
- region:ustask_categories:object-detectiontask_categories:roboticslicense:cc-by-4.0size_categories:100k
6d-pose-estimationtransparencymegaposebopsynthetic
βοΈ Technical Specs
- architecture
- null
- params billions
- null
- context length
- null
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- downloads
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