论文标题
housecat6d-一个大尺度多模式类别级别6D对象感知数据集，带有家用对象在现实的情况下
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
论文作者
论文摘要
估计6D对象姿势是3D计算机视觉的主要挑战。在成功实例级别的方法的基础上，研究正在向实际应用的类别级姿势估计转移。但是，当前类别级别的数据集的注释质量和姿势多样性都不足。解决此问题，我们介绍了一个新的类别级别6D姿势数据集HouseCat6d。它具有1）具有极化RGB和深度的多模式（RGBD+P），2）包括10个家庭类别的194个不同对象，包括两个具有光学挑战性的挑战，3）提供了高质量的姿势注释，其误差范围仅为1.35 mm至1.74 mm。该数据集还包括4）41个大型场景，具有全面的视点和遮挡覆盖范围，5）不含棋盘的环境以及6）密集的6d 6d并联机器人Grasp注释。此外，我们为领先类别级姿势估计网络提供基准结果。
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.
