Abstract
With the advent of RGB-D cameras, such as Kinect, it is now possible to benefit from both color and depth of the field of view in real time towards a better understanding of the scene. While such devices are providing appreciative depth information; captured depth map suffers from noticeable noise. Recent methods reach satisfactory results in depth recovery by focusing on color and depth images individually or cooperatively. In this paper, we propose a geometric approach to structurally model the scene by extracting a series of planes from the point cloud. The problem is formulated as an energy minimization function based on initial depth values calculated by modeling the scene using planes, and applying local filters on color image and depth map. The presented method is implemented and tested on simulation data and experimental results show its accurate and precise performance.
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Esfahani, M.A., Pourreza, H. (2017). Kinect Depth Recovery Based on Local Filters and Plane Primitives. In: Constanda, C., Dalla Riva, M., Lamberti, P., Musolino, P. (eds) Integral Methods in Science and Engineering, Volume 2. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-59387-6_6
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DOI: https://doi.org/10.1007/978-3-319-59387-6_6
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