Abstract

Meshes generated by range scanners are often incomplete and contain complex holes due to limited input coverage and occlusion. In this paper, we present an effective method to fill the gap regions on meshes by leveraging the templates inferred from the learning-based method. We first segment both source and template models into corresponding parts. Each part will be aligned with non-rigid deformation. We then modify the gap regions by “virtual” depth maps rendered using the aligned parts from newly selected viewpoints. Comparing with the template-based mesh completion approaches, our algorithm can generate natural appearances without any user interaction. Comparing with the state-of-the-art volumetric fusion methods, our approach supports selective blending, which only modifies the regions of interest and prevents bad template inference from impacting the source.

IEEE Xplore

Recommended citation: K. Chen, F. Yin, B. Wu, B. Du and T. Nguyen, "Mesh Completion with Virtual Scans," 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, 2021, pp. 3303-3307