SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation

• URL: http://arxiv.org/abs/2108.08367v1
• Date: Wed, 18 Aug 2021 19:49:29 GMT
• Title: SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation
• Authors: Yan Di, Fabian Manhardt, Gu Wang, Xiangyang Ji, Nassir Navab and Federico Tombari
• Abstract summary: SO-Pose is a framework for regressing all 6 degrees-of-freedom (6DoF) for the object pose in a cluttered environment from a single RGB image. We introduce a novel reasoning about self-occlusion, in order to establish a two-layer representation for 3D objects. Cross-layer consistencies that align correspondences, self-occlusion and 6D pose, we can further improve accuracy and robustness.
• Score: 98.83762558394345
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Directly regressing all 6 degrees-of-freedom (6DoF) for the object pose (e.g. the 3D rotation and translation) in a cluttered environment from a single RGB image is a challenging problem. While end-to-end methods have recently demonstrated promising results at high efficiency, they are still inferior when compared with elaborate P$n$P/RANSAC-based approaches in terms of pose accuracy. In this work, we address this shortcoming by means of a novel reasoning about self-occlusion, in order to establish a two-layer representation for 3D objects which considerably enhances the accuracy of end-to-end 6D pose estimation. Our framework, named SO-Pose, takes a single RGB image as input and respectively generates 2D-3D correspondences as well as self-occlusion information harnessing a shared encoder and two separate decoders. Both outputs are then fused to directly regress the 6DoF pose parameters. Incorporating cross-layer consistencies that align correspondences, self-occlusion and 6D pose, we can further improve accuracy and robustness, surpassing or rivaling all other state-of-the-art approaches on various challenging datasets.

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