Estimating the 6D pose of objects is a major 3D computer vision problem.
Since the promising outcomes from instance-level approaches, research heads
also move towards category-level pose estimation for more practical application
scenarios. However, unlike well-established instance-level pose datasets,
available category-level datasets lack annotation quality and provided pose
quantity. We propose the new category-level 6D pose dataset HouseCat6D
featuring 1) Multi-modality of Polarimetric RGB and Depth (RGBD+P), 2) Highly
diverse 194 objects of 10 household object categories including 2
photometrically challenging categories, 3) High-quality pose annotation with an
error range of only 1.35 mm to 1.74 mm, 4) 41 large-scale scenes with extensive
viewpoint coverage and occlusions, 5) Checkerboard-free environment throughout
the entire scene, and 6) Additionally annotated dense 6D parallel-jaw grasps.
Furthermore, we also provide benchmark results of state-of-the-art
category-level pose estimation networks