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.
@article{arxiv.2109.12266,
title = {Learning Stereopsis from Geometric Synthesis for 6D Object Pose Estimation},
author = {Jun Wu and Lilu Liu and Yue Wang and Rong Xiong},
journal= {arXiv preprint arXiv:2109.12266},
year = {2021}
}