Geometric Edge Description and Classification in Point Cloud Data with Application to 3D Object Recognition

Troels Bo Jørgensen, Anders Glent Buch, Dirk Kraft

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper addresses the detection of geometric edges on 3D shapes. We investigate the use of local point cloud features and cast the edge detection problem as a learning problem. We show how supervised learning techniques can be applied to an existing shape description in terms of local feature descriptors. We apply our approach to several well-known shape descriptors. As an additional contribution, we develop a novel shape descriptor, termed
Equivalent Circumference Surface Angle Descriptor or ECSAD, which is particularly suitable for capturing local surface properties near edges. Our proposed descriptor allows for both fast computation and fast processing by having a low dimension, while still producing highly reliable edge detections. Lastly, we use our features in a 3D object recognition application using a well-established benchmark. We show that our edge features allow for significant speedups while achieving state of the art results.
Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Computer Vision Theory and Applications
EditorsJosé Braz, Sebastiano Battiato, Francisco Imai
Volume1
PublisherInstitute for Systems and Technologies of Information, Control and Communication
Publication date11. Mar 2015
Pages333-340
ISBN (Electronic)978-989-758-089-5
DOIs
Publication statusPublished - 11. Mar 2015
Event10th International Conference on Computer Vision Theory and Applications - Berlin, Germany
Duration: 11. Mar 201514. Mar 2015

Conference

Conference10th International Conference on Computer Vision Theory and Applications
Country/TerritoryGermany
CityBerlin
Period11/03/201514/03/2015

Keywords

  • Edge Detection
  • Object Recognition
  • Pose Estimation

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