Performance evaluation of stereo for tele-presence

J. Mulligan, V. Isler, K. Daniilidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

Abstract

In an immersive tele-presence environment a 3D remote real scene is projected from the viewpoint of the local user. This 3D world is acquired through stereo reconstruction at the remote site. In this paper, we start a performance analysis of stereo algorithms with respect to the task of immersive visualization. As opposed to usual monocular image based rendering, we are also interested in the depth error in novel views because our rendering is stereoscopic. We describe an evaluation test-bed which provides a world-wide first available set of registered dense "ground-truth" laser data and image data from multiple views. We establish metrics for novel depth views that reflect discrepancies both in the image and in 3D-space. It is well known that stereo performance is affected by both erroneous matching as well as incorrect depth triangulation. We experimentally study the effects of occlusion and low texture on the distributions of the error metrics. Then, we algebraically predict the behavior of depth and novel projection error as a function of the camera set-up and the error in the disparity. These are first steps towards building a laboratory for psychophysical judgement of depth estimates which is the ultimate performance test of tele-presence stereo.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE International Conference on Computer Vision
Pages558-565
Number of pages8
Volume2
StatePublished - Jan 1 2001
Event8th International Conference on Computer Vision - Vancouver, BC, United States
Duration: Jul 9 2001Jul 12 2001

Other

Other8th International Conference on Computer Vision
CountryUnited States
CityVancouver, BC
Period7/9/017/12/01

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    Mulligan, J., Isler, V., & Daniilidis, K. (2001). Performance evaluation of stereo for tele-presence. In Proceedings of the IEEE International Conference on Computer Vision (Vol. 2, pp. 558-565)