Joint Estimation of Depth, Reflectance and Illumination for Depth Refinement

Kichang Kim, Akihiko Torii, Masatoshi Okutomi; Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2015, pp. 1-9

Abstract


In this paper we propose a method for joint estimation of depth, reflectance and illumination from a single RGB-D image for depth refinement. This is achieved by a simple optimization based approach with smoothness constraints on depth, reflectance and illumination. We introduce an adaptively weighted local similarity constraint for reflectance, a normalized spherical-harmonic model for illumination, and an edge-aware local smoothness constraint for depth. This allows us to generate high quality depth without additional processes such as pre-training of stochastic models or image segmentation. Experimental results demonstrate that our method estimates high quality depth in comparison with ground-truth data not only for laboratory conditions but also for complex real-world scenes.

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[bibtex]
@InProceedings{Kim_2015_ICCV_Workshops,
author = {Kim, Kichang and Torii, Akihiko and Okutomi, Masatoshi},
title = {Joint Estimation of Depth, Reflectance and Illumination for Depth Refinement},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {December},
year = {2015}
}