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Dense 3-D Structure from Image Sequences Using Probabilistic Depth Carving

Annie Yao, Andrew Calway, Dense 3-D Structure from Image Sequences Using Probabilistic Depth Carving. Proceedings of the 14th British Machine Vision Conference (BMVC 2003). ISBN 1 901725 23 5, pp. 211–220. September 2003. PDF, 304 Kbytes.

Abstract

We describe an algorithm to determine dense 3-D structure in a static scene from an image sequence captured by a moving camera. Metric camera motions are first determined using a recursive structure from motion algorithm based on tracked feature points. Dense depth information for a subset of key frames is then obtained using a novel probabilistic depth carving algorithm - analogous to space carving - in which depth probabilities obtained locally about the key frames are combined in 3-D space. An important component in this process is that opacity and occlusion relationships are modelled explicitly, enabling consistent combination of the depth probabilities. Results of experiments on a real sequence illustrate the effectiveness of the approach.

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