An efficient rotation and translation decoupled initialization from large field of view depth images

Abstract

Image and point cloud registration methods compute the relative pose between two images. Commonly used registration algorithms are iterative and rely on the assumption that the motion between the images is small. In this work, we propose a fast pose estimation technique to compute a rough estimate of large motions between depth images, which can be used as initialization to dense registration methods. The main idea is to explore the properties given by planar surfaces with co-visibility and their normals from two distinct viewpoints. We present, in two decoupled stages, the rotation and then the translation estimation, both based on the normal vectors orientation and on the depth. These two stages are efficiently computed by using low resolution depth images and without any feature extraction/matching. We also analyze the limitations and observabilty of this approach, and its relationship to ICP point-to-plane. Notably, if the rotation is observable, at least five degrees of freedom can be estimated in the worst case. To demonstrate the effectiveness of the method, we evaluate the initialization technique in a set of challenging scenarios, comprising simulated spherical images from the Sponza Atrium model benchmark and real spherical indoor sequences.