HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset With Household Objects in Realistic Scenarios

MCML Authors

Guangyao Zhai

→ Group Nassir Navab
 Computer Aided Medical Procedures & Augmented Reality

Nassir Navab

Prof. Dr.

Principal Investigator

Computer Aided Medical Procedures & Augmented Reality

Benjamin Busam

Prof. Dr.

Principal Investigator

Photogrammetry and Remote Sensing

Abstract

Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches, research is shifting towards category-level pose estimation for practical applications. Current category-level datasets, however, fall short in annotation quality and pose variety. Addressing this, we introduce HouseCat6D, a new category-level 6D pose dataset. It features 1) multi-modality with Polarimetric RGB and Depth (RGBD+P), 2) encompasses 194 diverse objects across 10 household cat-egories, including two photometrically challenging ones, and 3) provides high-quality pose annotations with an error range of only 1.35 mm to 1.74 mm. The dataset also includes 4) 41 large-scale scenes with comprehensive view-point and occlusion coverage,5) a checkerboard-free en-vironment, and 6) dense 6D parallel-jaw robotic grasp annotations. Additionally, we present benchmark results for leading category-level pose estimation networks.

inproceedings

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CVPR 2024

IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA, Jun 17-21, 2024.

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Authors

H. Jung • S.-C. Wu • P. Ruhkamp • G. Zhai • H. Schieber • G. Rizzoli • P. Wang • H. Zhao • L. Garattoni • D. Roth • S. Meier • N. Navab • B. Busam

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Links

DOI

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Research Areas

 B1 | Computer Vision

 C1 | Medicine