HouseCat6D
A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
HyunJun Jung∗, Guangyao Zhai∗, Shun-Cheng Wu∗, Patrick Ruhkamp∗, Hannah Schieber∗,
Giulia Rizzoli, Pengyuan Wang, Hongcheng Zhao, Lorenzo Garattoni, Sven Meier, Daniel Roth, Nassir Navab, Benjamin Busam
CVPR 2024 Highlight
This is the project website for the HouseCat6D dataset, an indoor dataset focusing on object pose estimation and grasp pose estimation.
HouseCat6D is a multi-modal category level 6D object pose and grasping dataset with highly diverse household object categories of different photometric complexity and a high number of varying scenes covering large viewpoint distributions. It comprises room-scale high-quality camera trajectories and object poses without markers in realistic scenarios including occlusions as well as dense grasping pose annotation. Data includes synchronized RGB, depth from active stereo, and polarimetric RGB+P images in scenes comprising objects without texture, strong reflections, or translucency.
Data Collection Facilities
ARTTRACK2 tracking system and sets of infrared marker bodies we used for our setup. Once at least four infrared spheres are detected from at least two cameras, the tracking system provides the pose of the marker body as transformation from tracker system base to marker body base.
Data Collection Procedure
(a): Pre-scanning 3D models. (b): Pivot calibration to calibrate measurement tip from the tracking body. (c): Pose annotation of objects using measurement tip. (d): Hand-Eye-Calibration to calibrate camera center of tracking body. (e): Camera trajectory recording (f): Post-processing step to reduce synchronization-induced trajectory error.
If you have used the dataset in your work or feel that this work has helped your research a bit, please kindly consider citing it:
@article{jung2022housecat6d,
title={HouseCat6D--A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios},
author={Jung, HyunJun and Zhai, Guangyao and Wu, Shun-Cheng and Ruhkamp, Patrick and Schieber, Hannah and Wang, Pengyuan and Rizzoli, Giulia and Zhao, Hongcheng and Meier, Sven Damian and Roth, Daniel and Navab, Nassir and others},
journal={arXiv preprint arXiv:2212.10428},
year={2022}
}
journal={arX preprint arXiv:2309.09