A Convex Relaxation Approach to Space Time Multi-view 3D Reconstruction

Martin R. Oswald, Daniel Cremers; Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2013, pp. 291-298

Abstract


We propose a convex relaxation approach to space-time 3D reconstruction from multiple videos. Generalizing the works [16], [8] to the 4D setting, we cast the problem of reconstruction over time as a binary labeling problem in a 4D space. We propose a variational formulation which combines a photoconsistency based data term with a spatiotemporal total variation regularization. In particular, we propose a novel data term that is both faster to compute and better suited for wide-baseline camera setups when photoconsistency measures are unreliable or missing. The proposed functional can be globally minimized using convex relaxation techniques. Numerous experiments on a variety of publically available data sets demonstrate that we can compute detailed and temporally consistent reconstructions. In particular, the temporal regularization allows to reduce jittering of voxels over time.

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[bibtex]
@InProceedings{R._2013_ICCV_Workshops,
author = {Martin R. Oswald and Daniel Cremers},
title = {A Convex Relaxation Approach to Space Time Multi-view 3D Reconstruction},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {June},
year = {2013}
}