  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.
