High-Speed Depth-Normal Measurement and Fusion Based on Multiband Sensing and Block Parallelization

A wide range of research areas have high expectations for the technology to measure 3D shapes, and to reconstruct the shape of a target object in detail from multiple data. In this study, we consider a high-speed shape measurement technology that realizes accurate measurements in dynamic scenes in which the target object is in motion or deforms, or where the measurement system itself is moving. We propose a measurement-method that sacrifices neither measurement density nor accuracy while realizing high speed. Many conventional 3D shape measurement systems employ only depth information to reconstruct a shape, which makes it difficult to capture the irregularities of an object's surface in detail. Meanwhile, methods that measure the surface normal to capture 3D shapes can reconstruct high-frequency components, although low-frequency components tend to include integration errors. Thus, depth information and surface normal information have a complementary relationship in 3D shape measurements. This study proposes a novel optical system that simultaneously measures the depth and normal information at high speed by waveband separation, and a method that reconstructs the high-density, high-accuracy 3D shape at high speed from the two obtained data types by block division. This paper describes the proposed optical system and reconstruction-method, and it evaluates the computation time and the accuracy of reconstruction using an actual measurement system. The results confirm that the high-speed measurement was conducted at 400 fps with pixel-wise measurement density, and a measurement accuracy with an average error of 1.61 mm.