Normal Estimation for Accurate 3D Mesh Reconstruction with Point Cloud Model Incorporating Spatial Structure
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.
Cite
Text
Hashimoto and Saito. "Normal Estimation for Accurate 3D Mesh Reconstruction with Point Cloud Model Incorporating Spatial Structure." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.Markdown
[Hashimoto and Saito. "Normal Estimation for Accurate 3D Mesh Reconstruction with Point Cloud Model Incorporating Spatial Structure." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/hashimoto2019cvprw-normal/)BibTeX
@inproceedings{hashimoto2019cvprw-normal,
title = {{Normal Estimation for Accurate 3D Mesh Reconstruction with Point Cloud Model Incorporating Spatial Structure}},
author = {Hashimoto, Taisuke and Saito, Masaki},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
pages = {54-63},
url = {https://mlanthology.org/cvprw/2019/hashimoto2019cvprw-normal/}
}