Robust dense reconstruction by range merging based on confidence estimation  

作　　者：Yadang CHEN Chuanyan HAO Wen WU Enhua WU 

机构地区：[1]School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China [2]School of Education Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China [3]Department of Computer and Information Science, Faculty of Science and Technology, University of Macao, Macao 999078, China [4]State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100864, China

出　　处：《Science China(Information Sciences)》2016年第9期18-28,共11页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant No.61272326);Startup Foundation for Introducing Talent of NUIST(Grant No.2243141601013)

摘　　要：Although the stereo matching problem has been extensively studied during the past decades, automatically computing a dense 3D reconstruction from several multiple views is still a difficult task owing to the problems of textureless regions, outliers, detail loss, and various other factors. In this paper, these difficult problems are handled effectively by a robust model that outputs an accurate and dense reconstruction as the final result from an input of multiple images captured by a normal camera. First, the positions of the camera and sparse 3D points are estimated by a structure-from-motion algorithm and we compute the range map with a confidence estimation for each image in our approach. Then all the range maps are integrated into a fine point cloud data set. In the final step we use a Poisson reconstruction algorithm to finish the reconstruction. The major contributions of the work lie in the following points： effective range-computation and confidence-estimation methods are proposed to handle the problems of textureless regions, outliers and detail loss. Then, the range maps are merged into the point cloud data in terms of a confidence-estimation. Finally, Poisson reconstruction algorithm completes the dense mesh. In addition, texture mapping is also implemented as a post-processing work for obtaining good visual effects. Experimental results are presented to demonstrate the effectiveness of the proposed approach.Although the stereo matching problem has been extensively studied during the past decades, automatically computing a dense 3D reconstruction from several multiple views is still a difficult task owing to the problems of textureless regions, outliers, detail loss, and various other factors. In this paper, these difficult problems are handled effectively by a robust model that outputs an accurate and dense reconstruction as the final result from an input of multiple images captured by a normal camera. First, the positions of the camera and sparse 3D points are estimated by a structure-from-motion algorithm and we compute the range map with a confidence estimation for each image in our approach. Then all the range maps are integrated into a fine point cloud data set. In the final step we use a Poisson reconstruction algorithm to finish the reconstruction. The major contributions of the work lie in the following points： effective range-computation and confidence-estimation methods are proposed to handle the problems of textureless regions, outliers and detail loss. Then, the range maps are merged into the point cloud data in terms of a confidence-estimation. Finally, Poisson reconstruction algorithm completes the dense mesh. In addition, texture mapping is also implemented as a post-processing work for obtaining good visual effects. Experimental results are presented to demonstrate the effectiveness of the proposed approach.

关 键 词：stereo matching 3D reconstruction textureless regions outliers details loss range map 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]