This paper deals with a data fusion technique for depth reconstruction which integrates regularization by variational methods with stochastic optimization based on Kalman filtering. A framework for the fusion of multiple regularized depth maps is proposed for on-line integration of many views of the visible scene. This kind of approach has some advantages in respect with similar ones, as it is stressed widely in the paper. It does not use optical flow, camera modeling or an explicit motion equation and can be used to fuse stochastically both sparse or dense depth data, obtaining reliable estimates in the whole image domain.

Date Published: 22 June 1994
PDF: 7 pages
Proc. SPIE 2233, Sensor Fusion and Aerospace Applications II, (22 June 1994); doi: 10.1117/12.179047

Published in SPIE Proceedings Vol. 2233:
Sensor Fusion and Aerospace Applications II
Nagaraj Nandhakumar, Editor(s)