Depth inpainting with Tensor Voting using local geometry

Range images captured from range scanning devices or reconstructed form optical cameras often suffer from missing regions due to occlusions, reflectivity, limited scanning area, sensor imperfections etc. In this paper, we propose a fast and simple algorithm for range map inpainting using Tensor Voting (TV) framework. From a single range image, we gather and analyze geometric information so as to estimate missing depth values. To deal with large missing regions, TV-based segmentation is initially employed as a cue for a region filling. Subsequently, we use 3D tensor voting for estimating different plane equations and pass depth estimates from all possible local planes that pass through a missing region. A final pass of tensor voting is performed to choose the best depth estimate for each point in the missing region. We demonstrate the effectiveness of our approach on synthetic as well as real data.