Open AccessAn iterative refinement method of point cloud for binocular vision 3D reconstruction
Author(s) -
HU Jun-wei,
Jifeng Sun,
Yinggang Li,
Qi Zhang,
Shuai Zhao,
Yibin Lin
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2078/1/012038
Subject(s) - point cloud , computer science , cloud computing , point (geometry) , computer vision , artificial intelligence , process (computing) , set (abstract data type) , image (mathematics) , dual (grammatical number) , function (biology) , iterative reconstruction , algorithm , mathematics , geometry , evolutionary biology , biology , art , literature , programming language , operating system
This paper introduces a new binocular stereo deep learning network based on point cloud, which can realize higher precision point cloud reconstruction through continuous iteration of the network. Our method directly carries out point cloud processing on the target, calculates the difference between the current depth map and the real depth, estimates the loss according to the predicted point cloud and the information of the dual view input image, and then uses the appropriate loss function to iteratively process the point cloud. In addition, we can customize the number of iterations to achieve higher precision point cloud effect. The proposed network basically achieves good results on KITTI data set.