A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

Creative Commons License

Vinodkumar P. K., Karabulut D., Avots E., Ozcinar C., Anbarjafari G.

Entropy, vol.25, no.4, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Review
  • Volume: 25 Issue: 4
  • Publication Date: 2023
  • Doi Number: 10.3390/e25040635
  • Journal Name: Entropy
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, INSPEC, Metadex, zbMATH, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: 3D object classification, 3D object detection, 3D object recognition, 3D object segmentation, deep learning
  • Yıldız Technical University Affiliated: No

Abstract

The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). Deep learning approaches have lately emerged as the preferred method for 3D segmentation problems as a result of their outstanding performance in 2D computer vision. As a result, many innovative approaches have been proposed and validated on multiple benchmark datasets. This study offers an in-depth assessment of the latest developments in deep learning-based 3D object recognition. We discuss the most well-known 3D object recognition models, along with evaluations of their distinctive qualities.