HouseCat6D - A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios - Jung, Hyun Jun; Wu, Shun Cheng; Ruhkamp, Patrick; Zhai, Guangyao; Schieber, Hannah; Rizzoli, Giulia; Wang, Pengyuan; Zhao, Hongcheng; Garattoni, Lorenzo; Roth, Daniel; Meier, Sven; Navab, Nassir; et al. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2024)
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.
CRIS-Team Scopus Importer:2024-11-01
Keywords: 6D Pose Labels; Accurate 3D Data Acquisition; Category Level 6D Pose Estimation; Dataset; Photometrically Challenging Objects; Robotic Grasping; Robotic Manipulation