Sparse Representations for Three-Dimensional Range Data Restoration

Abstract : Sparse representations of signals, in particular with learned dictionaries, are widely used for state-of-the-art audio, image, and video restoration. In this paper, the problem of denoising and occlusion restoration of 3D range data based on dictionary learning and sparse representations is explored. We consider the 3D surface obtained from a desktop range scanner as an image, where the value of each pixel represents the depth of a point on the 3D surface. Having this image, we apply techniques from dictionary learning and sparse representation to enhance the acquired 3D surface. These techniques use the spare decomposition of the overlapping patches in the image, over an adapted over-complete dictionary, for enhancing the data. We present experimental results of denoising 3D surfaces following this approach. We also propose an algorithm for filling the missing information regions on 3D scans and demonstrate its effectiveness. Our experimental results are on range data obtained from a low-cost structured-light range scanner.