摘要
Abstract
Multi-View Stereo is one of the important tasks in the field of computer vision,which aims to recover the structural infor-mation of a scene from images from multiple perspectives.However,due to the severe local inconsistencies in cost-volume aggregation,direct aggregation of geometrically adjacent costs can lead to serious misdirection.The existing methods either seek the optimal selective aggregation of two-dimensional space,or increase the means of aggregation,but they cannot effectively solve the geometric inconsistency of cost volume,resulting in poor accuracy and robustness of depth estimation.In order to solve this problem,a Collaborative Representa-tion for Multi-view Stereo(CRMVS)was proposed,which aimed to integrate the consistency information of the geometry with multiple modules and improve the depth estimation accuracy and robustness of the multi-view stereo matching task.Firstly,we use the improved Feature Pyramid Network(FPN)to enhance the feature extraction capability of the network.Secondly,we design a progressive weighted network module(PWN)to construct the cost body.Finally,we design a Geometric Cost Aggregation and Refinement Network Module(GCR)to accurately aggregate the cost body.Experimental results show that our method shows advanced performance on DTU,Tanks&Temple datasets.关键词
多视图立体匹配/特征金字塔/代价体/成本聚合Key words
Multi-View stereo/Feature pyramid/Cost volume/Cost aggregation分类
计算机与自动化
