research-article

PCEDNet: A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds

Authors:

Chems-Eddine Himeur,

Thibault Lejemble,

Thomas Pellegrini,

Mathias Paulin,

Nicolas MelladoAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 41, Issue 1

Article No.: 10, Pages 1 - 21

https://doi.org/10.1145/3481804

Published: 10 November 2021 Publication History

Abstract

In recent years, Convolutional Neural Networks (CNN) have proven to be efficient analysis tools for processing point clouds, e.g., for reconstruction, segmentation, and classification. In this article, we focus on the classification of edges in point clouds, where both edges and their surrounding are described. We propose a new parameterization adding to each point a set of differential information on its surrounding shape reconstructed at different scales. These parameters, stored in a Scale-Space Matrix (SSM), provide a well-suited information from which an adequate neural network can learn the description of edges and use it to efficiently detect them in acquired point clouds. After successfully applying a multi-scale CNN on SSMs for the efficient classification of edges and their neighborhood, we propose a new lightweight neural network architecture outperforming the CNN in learning time, processing time, and classification capabilities. Our architecture is compact, requires small learning sets, is very fast to train, and classifies millions of points in seconds.

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Index Terms

PCEDNet: A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Point-based models
      2. Shape analysis
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 41, Issue 1

February 2022

178 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3484929

Editor:
Carol O'Sullivan
Trinity College Dublin, Ireland

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Publication History

Published: 10 November 2021

Accepted: 01 August 2021

Revised: 01 June 2021

Received: 01 October 2020

Published in TOG Volume 41, Issue 1

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