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Sensors 2018, 18(2), 431; doi:10.3390/s18020431

A Review of Depth and Normal Fusion Algorithms

Center for Vision, Automation and Control, Austrian Institute of Technology, Vienna 1210, Austria
Institute of Computer Graphics and Vision, Graz University of Technology, Graz 8010, Austria
Author to whom correspondence should be addressed.
Received: 11 November 2017 / Revised: 21 December 2017 / Accepted: 26 January 2018 / Published: 1 February 2018
(This article belongs to the Section Physical Sensors)
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Geometric surface information such as depth maps and surface normals can be acquired by various methods such as stereo light fields, shape from shading and photometric stereo techniques. We compare several algorithms which deal with the combination of depth with surface normal information in order to reconstruct a refined depth map. The reasons for performance differences are examined from the perspective of alternative formulations of surface normals for depth reconstruction. We review and analyze methods in a systematic way. Based on our findings, we introduce a new generalized fusion method, which is formulated as a least squares problem and outperforms previous methods in the depth error domain by introducing a novel normal weighting that performs closer to the geodesic distance measure. Furthermore, a novel method is introduced based on Total Generalized Variation (TGV) which further outperforms previous approaches in terms of the geodesic normal distance error and maintains comparable quality in the depth error domain. View Full-Text
Keywords: depth reconstruction; surface normals; optimization; Total Generalized Variation; primal-dual algorithm; computational imaging; least squares depth reconstruction; surface normals; optimization; Total Generalized Variation; primal-dual algorithm; computational imaging; least squares

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Antensteiner, D.; Štolc, S.; Pock, T. A Review of Depth and Normal Fusion Algorithms. Sensors 2018, 18, 431.

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