Learning Stereopsis from Geometric Synthesis for 6D Object Pose Estimation

• URL: http://arxiv.org/abs/2109.12266v1
• Date: Sat, 25 Sep 2021 02:55:05 GMT
• Title: Learning Stereopsis from Geometric Synthesis for 6D Object Pose Estimation
• Authors: Jun Wu, Lilu Liu, Yue Wang and Rong Xiong
• Abstract summary: Current monocular-based 6D object pose estimation methods generally achieve less competitive results than RGBD-based methods. This paper proposes a 3D geometric volume based pose estimation method with a short baseline two-view setting. Experiments show that our method outperforms state-of-the-art monocular-based methods, and is robust in different objects and scenes.
• Score: 11.999630902627864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current monocular-based 6D object pose estimation methods generally achieve less competitive results than RGBD-based methods, mostly due to the lack of 3D information. To make up this gap, this paper proposes a 3D geometric volume based pose estimation method with a short baseline two-view setting. By constructing a geometric volume in the 3D space, we combine the features from two adjacent images to the same 3D space. Then a network is trained to learn the distribution of the position of object keypoints in the volume, and a robust soft RANSAC solver is deployed to solve the pose in closed form. To balance accuracy and cost, we propose a coarse-to-fine framework to improve the performance in an iterative way. The experiments show that our method outperforms state-of-the-art monocular-based methods, and is robust in different objects and scenes, especially in serious occlusion situations.

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