Geometry reconstruction and fusion for large-scale, featureless, and specular surfaces

3D reconstruction is greatly important for perceptual understanding of applications in robotics, computer graphics, and human-computer interaction. Though tremendous researches have been carried out for geometry reconstruction, there are still many challenges to cope with the large-scale, featureless, and specular surfaces. In this paper, we are motivated to propose a unified framework of geometry reconstruction and fusion for surfaces with the above characteristics. For the geometry reconstruction of specular surfaces, we present a method combining Stereo Phase Measuring Deflectometry (SPMD) with modal wavefront reconstruction to retrieve a reliable geometric surface. For the geometry fusion of largescale featureless surfaces, we put forward an offline calibration approach exploiting the Structure-from-Motion (SfM) to merge point clouds reconstructed from different viewpoints. To evaluate the effectiveness of our method, we build a specific SPMD camera system, design a large-scale calibration board, and set up a test scene with an industrial robot and car body. The experiments show that our method can achieve a good performance in terms of reconstruction accuracy of around 0.02mm and fusion accuracy of around 2mm for a large surface.