Hierarchical Edge Aware Learning for 3D Point Cloud

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This paper proposes an innovative approach to Hierarchical Edge Aware 3D Point Cloud Learning (HEA-Net) that seeks to address the challenges of noise in point cloud data, and improve object recognition and segmentation by focusing on edge features. In this study, we present an innovative edge-aware learning methodology, specifically designed to enhance point cloud classification and segmentation. Drawing inspiration from the human visual system, the concept of edge-awareness has been incorporated into this methodology, contributing to improved object recognition while simultaneously reducing computational time. Our research has led to the development of an advanced 3D point cloud learning framework that effectively manages object classification and segmentation tasks. A unique fusion of local and global network learning paradigms has been employed, enriched by edge-focused local and global embeddings, thereby significantly augmenting the model’s interpretative prowess. Further, we have applied a hierarchical transformer architecture to boost point cloud processing efficiency, thus providing nuanced insights into structural understanding. Our approach demonstrates significant promise in managing noisy point cloud data and highlights the potential of edge-aware strategies in 3D point cloud learning. The proposed approach is shown to outperform existing techniques in object classification and segmentation tasks, as demonstrated by experiments on ModelNet40 and ShapeNet datasets.

Original languageEnglish
Title of host publicationAdvances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings
EditorsBin Sheng, Lei Bi, Jinman Kim, Nadia Magnenat-Thalmann, Daniel Thalmann
Publication date2024
ISBN (Print)9783031500688
Publication statusPublished - 2024
Event40th Computer Graphics International Conference, CGI 2023 - Shanghai, China
Duration: 28 Aug 20231 Sep 2023


Conference40th Computer Graphics International Conference, CGI 2023
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

    Research areas

  • 3D Point Cloud, Classification, Edge Learning, Segmentation

ID: 385796082