A learned detector method for point cloud registration

Abstract

In this paper, we propose a Detector-Net method for point cloud registration which learns a 3D feature detector of a specific descriptor. Different from the traditional detectors, deep neural network is used to generate this detector and manual annotation of feature points is not required. Instead, we leverage the aligned point cloud to deduce distinguishing points to generate training data. The indoor point cloud dataset is used as the training set, and experimental results show that the Detector-Net has better accuracy among traditional detectors.