Noisy Depth Maps Fusion for Multiview Stereo Via Matrix Completion

This paper introduces a general framework to fuse noisy point clouds from multiview images of the same object. We solve this classical vision problem using a newly emerging signal processing technique known as matrix completion. With this framework, we construct the initial incomplete matrix from th...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE journal of selected topics in signal processing Ročník 6; číslo 5; s. 566 - 582
Hlavní autori: Deng, Yue, Liu, Yebin, Dai, Qionghai, Zhang, Zengke, Wang, Yao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.09.2012
Predmet:
Cameras
Compressive sensing
Estimation
fusion
matrix completion
multiview stereo (MVS)
Noise
Noise measurement
point cloud
Robustness
Three dimensional displays
Vectors
ISSN:1932-4553, 1941-0484
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This paper introduces a general framework to fuse noisy point clouds from multiview images of the same object. We solve this classical vision problem using a newly emerging signal processing technique known as matrix completion. With this framework, we construct the initial incomplete matrix from the observed point clouds by all the cameras, with the invisible points by any camera denoted as unknown entries. The observed points corresponding to the same object point are put into the same row. When properly completed, the recovered matrix should have rank one, since all the columns describe the same object. Therefore, an intuitive approach to complete the matrix is by minimizing its rank subject to consistency with observed entries. In order to improve the fusion accuracy, we propose a general noisy matrix completion method called log-sum penalty completion (LPC), which is particularly effective in removing outliers. Based on the majorization-minimization algorithm (MM), the non-convex LPC problem is effectively solved by a sequence of convex optimizations. Experimental results on both point cloud fusion and MVS reconstructions verify the effectiveness of the proposed framework and the LPC algorithm.
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2012.2195472