A Variational Framework for Structure from Motion in Omnidirectional Image Sequences
We address the problem of depth and ego-motion estimation from omnidirectional images. We propose a correspondence-free structure from motion problem for images mapped on the 2-sphere. A novel graph-based variational framework is proposed for depth estimation. The problem is cast into a TV-L1 optimization problem that is solved by fast graph-based optimization techniques. The ego-motion is then estimated directly from the depth information without computation of the optical flow. Both problems are addressed jointly in an iterative algorithm that alternates between depth and ego-motion estimation for fast computation of the 3D information. Experimental results demonstrate the effective performance of the proposed algorithm for 3D reconstruction from synthetic and natural omnidirectional images.
Record created on 2009-11-23, modified on 2016-08-08