Scaled-neighborhood Patches Fusion for Multi-view Stereopsis

被引：0

作者：

An, Ning ^{[1
]}

He, Yicong ^{[1
]}

Dong, Hang ^{[1
]}

Wang, Fei ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China

来源：

2016 7TH INTERNATIONAL CONFERENCE ON MECHANICAL, INDUSTRIAL, AND MANUFACTURING TECHNOLOGIES (MIMT 2016) | 2016年 / 54卷

关键词：

D O I：

10.1051/matecconf/20165408006

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, we present a multi-view stereo reconstruction approach which fuses scaled-neighborhood information. PMVS proposed by Furukawa is one of the most excellent algorithms, and it has a good performance on many datasets both the accuracy and the completeness. However, there are still further improvements on this algorithm. PMVS cannot perform well in the presence of slanted surfaces, which are usually imaged at oblique angles. According to these aspects, on the one hand we propose to estimate the initial normal of every seed patch via fitting quadrics with scaled-neighborhood patches, which greatly improves the accuracy of the normal. On the other hand, we present to compute scaled-window for the further optimization based on texture. And it has been tested that employing the scaled-window will dramatically smooth the surfaces and enhance the reconstruction precision.

引用

页数：6

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