  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 categories,
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 viewpoint and
occlusion coverage, 5) a checkerboard-free environment, and 6) dense 6D
parallel-jaw robotic grasp annotations. Additionally, we present benchmark
results for leading category-level pose estimation networks.
