Title: Fast Depth Extraction from A Single Image
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| Authors: He, L; Dong, QL; Wang, GH
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| Author Full Names: He, Lei; Dong, Qiulei; Wang, Guanghui
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| Source: INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 13 10.1177/1729881416663370 NOV 14 2016
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| Language: English
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| Abstract: Predicting depth from a single image is an important problem for understanding the 3-D geometry of a scene. Recently, the nonparametric depth sampling (Depth Transfer) has shown great potential in solving this problem, and its two key components are a Scale Invariant Feature Transform (SIFT) flow-based depth warping between the input image and its retrieved similar images and a pixel-wise depth fusion from all warped depth maps. In addition to the inherent heavy computational load in the SIFT flow computation even under a coarse-to-fine scheme, the fusion reliability is also low due to the low discriminativeness of pixel-wise description nature. This article aims at solving these two problems. First, a novel sparse SIFT flow algorithm is proposed to reduce the complexity from subquadratic to sublinear. Then, a reweighting technique is introduced where the variance of the SIFT flow descriptor is computed at every pixel and used for reweighting the data term in the conditional Markov random fields. Our proposed depth transfer method is tested on the Make3D Range Image Data and NYU Depth Dataset V2. It is shown that, with comparable depth estimation accuracy, our method is 2-3 times faster than the Depth Transfer.
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| ISSN: 1729-8814
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| IDS Number: EL7PY
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| Unique ID: WOS:000394814200001
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