Normal Estimation for 3D Point Clouds via Local Plane Constraint and Multi-scale Selection

10/18/2019
by   Jun Zhou, et al.
20

In this paper, we propose a normal estimation method for unstructured 3D point clouds. In this method, a feature constraint mechanism called Local Plane Features Constraint (LPFC) is used and then a multi-scale selection strategy is introduced. The LPEC can be used in a single-scale point network architecture for a more stable normal estimation of the unstructured 3D point clouds. In particular, it can partly overcome the influence of noise on a large sampling scale compared to the other methods which only use regression loss for normal estimation. For more details, a subnetwork is built after point-wise features extracted layers of the network and it gives more constraints to each point of the local patch via a binary classifier in the end. Then we use multi-task optimization to train the normal estimation and local plane classification tasks simultaneously.Also, to integrate the advantages of multi-scale results, a scale selection strategy is adopted, which is a data-driven approach for selecting the optimal scale around each point and encourages subnetwork specialization. Specifically, we employed a subnetwork called Scale Estimation Network to extract scale weight information from multi-scale features. More analysis is given about the relations between noise levels, local boundary, and scales in the experiment. These relationships can be a better guide to choosing particular scales for a particular model. Besides, the experimental result shows that our network can distinguish the points on the fitting plane accurately and this can be used to guide the normal estimation and our multi-scale method can improve the results well. Compared to some state-of-the-art surface normal estimators, our method is robust to noise and can achieve competitive results.

READ FULL TEXT

page 7

page 8

page 10

page 11

research
12/03/2018

Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks

In this paper, we propose a normal estimation method for unstructured 3D...
research
10/13/2017

PCPNET: Learning Local Shape Properties from Raw Point Clouds

In this paper, we propose a deep-learning based approach for estimating ...
research
07/23/2022

GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation

We propose a precise and efficient normal estimation method that can dea...
research
08/04/2023

MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning

Estimating surface normals from 3D point clouds is critical for various ...
research
09/07/2023

A boundary-aware point clustering approach in Euclidean and embedding spaces for roof plane segmentation

Roof plane segmentation from airborne LiDAR point clouds is an important...
research
05/02/2017

Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity

In this paper we present a scalable approach for robustly computing a 3D...
research
04/15/2019

Differentiable Iterative Surface Normal Estimation

This paper presents an end-to-end differentiable algorithm for anisotrop...

Please sign up or login with your details

Forgot password? Click here to reset