Depth estimation plays an important role in many computer vision and computer graphics applications. Existing depth measurement techniques are still complex and restrictive. In this paper, we present a novel technique for inferring depth measurements via depth from defocus using active quasi-random point projection patterns. A quasi-random point projection pattern is projected onto the scene of interest, and each projection point in the image captured by a cellphone camera is analyzed using a deep learning model to estimate the depth at that point. The proposed method has a relatively simple setup, consisting of a camera and a projector, and enables depth inference from a single capture. We evaluate the proposed method both quantitatively and qualitatively and demonstrate strong potential for simple and efficient depth sensing.
CITATION STYLE
Ma, A., Wong, A., & Clausi, D. (2017). Depth from defocus via active quasi-random point projections: A deep learning approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10317 LNCS, pp. 35–42). Springer Verlag. https://doi.org/10.1007/978-3-319-59876-5_5
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