Complex Surface Reconstruction Based on Fusion of Surface Normals and Sparse Depth Measurement

Abstract

Precision measurement and reconstruction of detailed surfaces topography is a challenging task for non-diffuse complex parts. Although coordinate measurement machines (CMM) with the touch-trigger probe are widely used in current industry, the measurement efficiency limits their application in the measurement of complex surfaces. This article proposes a multisensor data fusion strategy by integrating the technical merits of CMM and photometric stereo (PS) to achieve multiscale reconstruction of a complex surface with high efficiency. Considering the complementary measurement characteristics of the two approaches, the sparse points from CMM are used to provide global shape information, and the high-resolution surface normal map from PS is used to provide local detailed structure. A multistage neural network is then proposed to fuse these two kinds of modality information such that the global features from the sparse points and the local features from the surface normal map are fused in a coarse-to-fine multistage process so as to make the training process more stable and the reconstruction more accurate. To enhance the generality of the fusion neural network, a synthetic training data set is also designed to include a large variety of multiscale features enriched surfaces. Experiments are conducted to verify the effectiveness of the proposed multisensor fusion strategy in accurate reconstruction of complex surfaces with high efficiency.