LGP-Net: Local Geometry Preserving Network for Point Cloud Completion

Abstract

Point clouds captured in real-world applications are often in-complete due to the limited sensor resolution, viewpoint, and occlusion. Therefore, recovering the completion point clouds from incomplete ones becomes an important work in many practical applications. However, most of previous work only focus on the point-to-point relationship between the reconstructed point cloud and groundtruth(GT), not fully exploring their local geometry relationship, which results in the inaccu-rate and nonuniform local details. To solve this problem, we propose a novel local geometry preserving point cloud com-pletion network(LGP-Net). By promoting the consistency of local geometry features between the reconstruction and GT, the proposed LGP-Net can preserve more accurate local de-tails. To further explore the local geometry correlation at dif-ferent scales, a multi-scale local geometry consistency is also proposed. Moreover, the consistency between the features at different scales are proposed to exploit the correlation of features under different resolutions. Quantitative and qualitative results on the benchmark dataset demonstrate that our LGP-Net achieves superior performance over several state-of-the-art methods significantly.