Towards real-time stereo using non-uniform image sampling and sparse dynamic programming

Michel Sarkis, Klaus Diepold

Research output: Contribution to conferencePaperpeer-review

5 Scopus citations

Abstract

Constructing the 3D mesh of a scene from stereo images is a major task in computer vision. It usually involves several steps including stereo matching and meshing. Unfortunately, the time required to generate the 3D mesh is time demanding due to the large amount of pixels to be processed. In this work, we propose a framework to accelerate the overall process. The key issue is to first reduce the number of pixels by approximating an image with a content adaptive mesh. The nodes of the mesh are sparse and they represent the non-uniform samples of the image. To benefit from the reduced set of pixels, we formulate a dynamic programming based stereo matching algorithm which computes the depth only at the sparse samples. We then show by setting up some tests using some real images that the non-uniform samples are sufficient to recover the original dense depth map of the scene by interpolating them using the mesh. The results obtained also show that the employment of the proposed strategy reduces the overall processing time of stereo matching to more than 50% of the original time. We are now able to construct scenes in real-time using less computational resources.

Original languageEnglish
Pages191-198
Number of pages8
StatePublished - 2008
Event4th International Symposium on 3D Data Processing, Visualization and Transmission, 3DPVT 2008 - Atlanta, United States
Duration: 18 Jun 200820 Jun 2008

Conference

Conference4th International Symposium on 3D Data Processing, Visualization and Transmission, 3DPVT 2008
Country/TerritoryUnited States
CityAtlanta
Period18/06/0820/06/08

Fingerprint

Dive into the research topics of 'Towards real-time stereo using non-uniform image sampling and sparse dynamic programming'. Together they form a unique fingerprint.

Cite this