HouseCat6D - A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
HyunJun Jung, Shun-Cheng Wu, Patrick Ruhkamp, Guangyao Zhai, Hannah Schieber, Giulia Rizzoli, Pengyuan Wang, Hongcheng Zhao, Lorenzo Garattoni, Daniel Roth, Sven Meier, Nassir Navab, Benjamin Busam
摘要
5 Toyota Motor Europe 6 3dwe.ai https://sites.google.com/view/housecat6d Figure 1. HouseCat6D is a multi-modal category level 6D object pose and grasping dataset with highly diverse household object categories of different photometric complexity and a high number of varying scenes covering large viewpoint distributions. It comprises room-scale high-quality camera trajectories and object poses without markers in realistic scenarios including occlusions as well as dense grasping pose annotation. Data includes synchronized RGB, depth from active stereo, and polarimetric RGB+P images in scenes comprising objects without texture, strong reflections, or translucency.
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引用它的顶会 Paper19
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- 6-DOF GraspNet: Variational Grasp Generation for Object ManipulationArsalan Mousavian, Clemens Eppner, Dieter FoxICCV 2019 · 被引用 673 次
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- Explaining the Ambiguity of Object Detection and 6D Pose From Visual DataFabian Manhardt, Diego Martín Arroyo, Christian Rupprecht, Benjamin Busam 等ICCV 2019 · 被引用 139 次
- Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New DatasetYanjie Ze, Xiaolong WangNeurIPS 2022 · 被引用 104 次
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