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

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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


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.