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

Taisuke Hashimoto, Masaki Saito; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 54-63

Abstract


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 Opoint networkO and a Ovoxel networkO. 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.

Related Material


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[bibtex]
@InProceedings{Hashimoto_2019_CVPR_Workshops,
author = {Hashimoto, Taisuke and Saito, Masaki},
title = {Normal Estimation for Accurate 3D Mesh Reconstruction with Point Cloud Model Incorporating Spatial Structure},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}