INENet: Inliers Estimation Network With Similarity Learning for Partial Overlapping Registration

Abstract

Point cloud registration is a key problem in the application of computer vision to robotics, autopilot and other fields. However, because the object is partially covered up or the resolution of 3D scanners is different, point clouds collected by the same sense may be inconsistent and even incomplete. Inspired by the recently proposed learning-based approaches, we propose Inliers Estimation Network (INENet) which includes a self-designed threshold prediction network and a probability estimation network with adaptive similarity mutual attention to help to find the overlapping area of the point clouds. In order to solve the above problems, we divide the partially overlapping point cloud registration task into two sub-tasks: overlapping areas detection and registration. The threshold prediction network can automatically calculate the threshold according to the input point clouds, and then the probability estimation network estimates the overlapping points by using threshold. The advantages of the proposed approach include: (1) threshold prediction network avoids bias and the complexity of manually adjusting the threshold. (2) Probability estimation network with similarity matrix can deeply fuse the information between a pair of point clouds, which is helpful to improve the accuracy. (3) INENet can be easily integrated into other overlapping region sensitive algorithms and without adjusting parameters. We conduct experiments on the ModelNet40, S3DIS and 3DMatch data sets. Specifically, the rotation error of the registration algorithm integrated with INENet is improved by at least 25% compared with direct partial overlap registration, our method improves the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{1} $ </tex-math></inline-formula> score by 5% and has better anti-noise ability compared with the existing overlap detection methods, showing the effectiveness of the proposed method.