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Normal Estimation for Accurate 3D Mesh Reconstruction with Point Cloud Model Incorporating Spatial Structure

In this paper, we propose a network that can accurately infer normal vectors from a point cloud without sacrificing inference speed. The key idea of our model is to introduce a voxel structure to extract spatial features from a given point cloud. Specifically, unlike the other existing methods directly exploiting point clouds, our model leverages two subnetworks called a “point network” and a “voxel network”. The point network extracts local features of a surface from a point cloud, whereas the voxel network transforms the point cloud into voxels and encodes the spatial features from them. The experimental results demonstrate the effectiveness of our method.