Decoupled deep hough voting for point cloud registration  

作　　者：Mingzhi YUAN Kexue FU Zhihao LI Manning WANG 

机构地区：[1]Digital Medical Research Center,School of Basic Medical Sciences,Fudan University,Shanghai 200032,China [2]Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention,Shanghai 200032,China

出　　处：《Frontiers of Computer Science》2024年第2期147-155,共9页中国计算机科学前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No.62076070);the Science and Technology Innovation Action Plan of Shanghai (No.23S41900400).

摘　　要：Estimating rigid transformation using noisy correspondences is critical to feature-based point cloud registration.Recently,a series of studies have attempted to combine traditional robust model fitting with deep learning.Among them,DHVR proposed a hough voting-based method,achieving new state-of-the-art performance.However,we find voting on rotation and translation simultaneously hinders achieving better performance.Therefore,we proposed a new hough voting-based method,which decouples rotation and translation space.Specifically,we first utilize hough voting and a neural network to estimate rotation.Then based on good initialization on rotation,we can easily obtain accurate rigid transformation.Extensive experiments on 3DMatch and 3DLoMatch datasets show that our method achieves comparable performances over the state-of-the-art methods.We further demonstrate the generalization of our method by experimenting on KITTI dataset.

关 键 词：point cloud registration robust model fitting deep learning hough voting 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]