6DoF Pose Estimation of Objects Using Position-Dependent Appearance Change Due to Perspective

Abstract

The mainstream approach for both instance- and category-level pose estimation tasks is to detect the object as a preprocessing step and to estimate the pose from only local information focused on the object. The appearance of the object varies, even if the pose is the same, due to perspective effects. This is one of the reasons for the difficulty in learning the model. The effect of perspective is an unique feature of the object's position. In this paper, we propose a method to convert this into a position cue, which is a feature for pose estimation. Experimental results show that the use of position cue improves the performance of both instance- and category-level pose estimation tasks by 1-5%.