A Point-Cloud-Based Multiview Stereo Algorithm for Free-Viewpoint Video

Authors:
Yebin Liu;Qionghai Dai;Wenli Xu
Affiliations:
Tsinghua University, Beijing;Tsinghua University, Beijing;Tsinghua University, Beijing
Venue:
IEEE Transactions on Visualization and Computer Graphics
Year:
2010

Citing 0
Cited 5

Point-cloud refinement via exact matching

ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
3D object relighting based on multi-view stereo and image based lighting techniques

ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Multi-view stereo reconstruction with high dynamic range texture

ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Iterative cage-based registration from multi-view silhouettes

Proceedings of the 10th European Conference on Visual Media Production
A flexible architecture for multi-view 3DTV based on uncalibrated cameras

Journal of Visual Communication and Image Representation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a robust multiview stereo (MVS) algorithm for free-viewpoint video. Our MVS scheme is totally point-cloud-based and consists of three stages: point cloud extraction, merging, and meshing. To guarantee reconstruction accuracy, point clouds are first extracted according to a stereo matching metric which is robust to noise, occlusion, and lack of texture. Visual hull information, frontier points, and implicit points are then detected and fused with point fidelity information in the merging and meshing steps. All aspects of our method are designed to counteract potential challenges in MVS data sets for accurate and complete model reconstruction. Experimental results demonstrate that our technique produces the most competitive performance among current algorithms under sparse viewpoint setups according to both static and motion MVS data sets.