3D Point Cloud for Objects and Scenes Classification, Recognition, Segmentation, and Reconstruction: A Review

Authors

  • Omar Elharrouss Department of Computer Science and Engineering, Qatar University, Doha, Qatar https://orcid.org/0000-0002-5341-5440
  • Kawther Hassine Higher School of Communications of Tunis, University of Carthage, Tunis, Tunisia
  • Ayman Zayyan Department of Computer Science and Engineering, Qatar University, Doha, Qatar
  • Zakariyae Chatri Department of Informatics, University of Sidi Mohamed Ben Ab-dellah, Fez, Morocco
  • Somaya Al-Maadeed Department of Computer Science and Engineering, Qatar University, Doha, Qatar
  • Khalid Abualsaud Department of Computer Science and Engineering, Qatar University, Doha, Qatar

DOI:

https://doi.org/10.37256/ccds.4220232722

Keywords:

point cloud, 3D point cloud, 3D object recognition, 3D object segmentation, 3D object and scene reconstruction

Abstract

Three-dimensional (3D) point cloud analysis has become one of the attractive subjects in realistic imaging and machine visions due to its simplicity, flexibility and powerful capacity of visualization. Actually, the representation of scenes and buildings using 3D shapes and formats leveraged many applications among which automatic driving, scenes and objects reconstruction, etc. Nevertheless, working with this emerging type of data has been a challenging task for objects representation, scenes recognition, segmentation, and reconstruction. In this regard, a significant effort has recently been devoted to developing novel strategies, using different techniques such as deep learning models. To that end, we present in this paper a comprehensive review of existing tasks on 3D point cloud: a well-defined taxonomy of existing techniques is performed based on the nature of the adopted algorithms, application scenarios, and main objectives. Various tasks performed on 3D point could data are investigated, including objects and scenes detection, recognition, segmentation, and reconstruction. In addition, we introduce a list of used datasets, discuss respective evaluation metrics, and compare the performance of existing solutions to better inform the state-of-the-art and identify their limitations and strengths. Lastly, we elaborate on current challenges facing the subject of technology and future trends attracting considerable interest, which could be a starting point for upcoming research studies.

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Published

2023-06-12

How to Cite

1.
Elharrouss O, Kawther Hassine, Ayman Zayyan, Zakariyae Chatri, Somaya Al-Maadeed, Khalid Abualsaud. 3D Point Cloud for Objects and Scenes Classification, Recognition, Segmentation, and Reconstruction: A Review. Cloud Computing and Data Science [Internet]. 2023 Jun. 12 [cited 2024 Oct. 5];4(2):134-60. Available from: https://ojs.wiserpub.com/index.php/CCDS/article/view/2722