DONet: Learning Category-Level 6D Object Pose and Size Estimation from Depth Observation

• URL: http://arxiv.org/abs/2106.14193v1
• Date: Sun, 27 Jun 2021 10:41:50 GMT
• Title: DONet: Learning Category-Level 6D Object Pose and Size Estimation from Depth Observation
• Authors: Haitao Lin, Zichang Liu, Chilam Cheang, Lingwei Zhang, Yanwei Fu, Xiangyang Xue
• Abstract summary: We propose a method of Category-level 6D Object Pose and Size Estimation (COPSE) from a single depth image. Our framework makes inferences based on the rich geometric information of the object in the depth channel alone. Our framework competes with state-of-the-art approaches that require labeled real-world images.
• Score: 53.55300278592281
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a method of Category-level 6D Object Pose and Size Estimation (COPSE) from a single depth image, without external pose-annotated real-world training data. While previous works exploit visual cues in RGB(D) images, our method makes inferences based on the rich geometric information of the object in the depth channel alone. Essentially, our framework explores such geometric information by learning the unified 3D Orientation-Consistent Representations (3D-OCR) module, and further enforced by the property of Geometry-constrained Reflection Symmetry (GeoReS) module. The magnitude information of object size and the center point is finally estimated by Mirror-Paired Dimensional Estimation (MPDE) module. Extensive experiments on the category-level NOCS benchmark demonstrate that our framework competes with state-of-the-art approaches that require labeled real-world images. We also deploy our approach to a physical Baxter robot to perform manipulation tasks on unseen but category-known instances, and the results further validate the efficacy of our proposed model. Our videos are available in the supplementary material.

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