Importance of structured-light based one-shot scanning technique is increasing because of its simple system configuration and ability of capturing moving objects. One severe limitation of the technique is that it can capture only sparse shape, but not high frequency shapes, because certain area of projection pattern is required to encode spatial information. In this paper, we propose a technique to recover high-frequency shapes by using shading information, which is captured by one-shot RGB-D sensor based on structured light with single camera. Since color image comprises shading information of object surface, high-frequency shapes can be recovered by shape from shading techniques. Although multiple images with different lighting positions are required for shape from shading techniques, we propose a learning based approach to recover shape from a single image. In addition, to overcome the problem of preparing sufficient amount of data for training, we propose a new data augmentation method for high-frequency shapes using synthetic data and domain adaptation. Experimental results are shown to confirm the effectiveness of the proposed method.
翻译:基于结构光的单发扫描技术的重要性正因其简单的系统配置和捕捉移动物体的能力而日益增强。该技术的一个严重局限性是,它只能捕捉稀有的形状,但不能捕捉高频形状,因为需要某些投影模式领域来编码空间信息。在本文中,我们建议采用一种技术,利用阴影信息来恢复高频形状,这种信息由光线以单一照相机结构为基础的一发RGB-D传感器捕获。由于彩色图像包括物体表面的阴影信息,因此,高频形状可以通过阴影技术的形状来恢复。虽然阴影技术的形状需要多个光线位置不同的图像,但我们建议采用基于学习的方法从单一图像中恢复形状。此外,为了克服为培训准备足够数据的问题,我们提出了一种利用合成数据和域适应的高频形状的新的数据增强方法。实验结果可以证实拟议方法的有效性。