An Efficient Technique for Filtering of 3D Cluttered Surfaces

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

3D Object recognition is a rapidly growing research field in the area of computer vision. The presence of cluttered surfaces is the main obstacle to object recognition. In this paper, we propose to present a technique for automatic filtering of cluttered surfaces. First, the technique clusters a point cloud and then based on three features that are size, distance and spatial information, cluttered surfaces are separated. To the best of our knowledge, this is the first technique that can remove any cluttered surface (plane or irregular) from a point cloud. We have experimented on two complex RGBD datasets containing heavily cluttered surfaces and using the proposed metric, we measure the remaining cluttered surfaces after filtering of a point cloud. The proposed clutter filtering has removed 87.60% and 89.68% cluttered surfaces for Challenge and Willow datasets respectively.
Original languageEnglish
Title of host publicationArtificial Intelligence and Soft Computing
Subtitle of host publication20th International Conference, ICAISC 2021, Virtual Event, June 21–23, 2021, Proceedings, Part II
EditorsLeszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek M. Zurada
PublisherSpringer
Pages36–43
Number of pages8
ISBN (Electronic)9783030878979
ISBN (Print)9783030878962
DOIs
Publication statusPublished - 6 Oct 2021
Event20th International Conference on Artificial Intelligence and Soft Computing - Virtual
Duration: 21 Jun 202123 Jun 2021

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12855
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Artificial Intelligence and Soft Computing
Abbreviated titleICAISC 2021
Period21/06/2123/06/21

Keywords

  • Cluttered surface
  • Cluttered surface filtering
  • 3D object recognition

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