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Importance of structured-light based one-shot scanning technique isincreasing because of its simple system configuration and ability of capturingmoving objects. One severe limitation of the technique is that it can captureonly sparse shape, but not high frequency shapes, because certain area ofprojection pattern is required to encode spatial information. In this paper, wepropose a technique to recover high-frequency shapes by using shadinginformation, which is captured by one-shot RGB-D sensor based on structuredlight with single camera. Since color image comprises shading information ofobject surface, high-frequency shapes can be recovered by shape from shadingtechniques. Although multiple images with different lighting positions arerequired for shape from shading techniques, we propose a learning basedapproach to recover shape from a single image. In addition, to overcome theproblem of preparing sufficient amount of data for training, we propose a newdata augmentation method for high-frequency shapes using synthetic data anddomain adaptation. Experimental results are shown to confirm the effectivenessof the proposed method.

High-frequency shape recovery from shading by CNN and domain adaptation