mlsanityData to 7 Oct 2026

Deep Learning on 3D Semantic Segmentation: A Detailed Review

arXiv 2411.02104 ↗First posted 4 Nov 2024cs.CVcited by 6

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Abstract (arXiv)

In this paper an exhaustive review and comprehensive analysis of recent and former deep learning methods in 3D Semantic Segmentation (3DSS) is presented. In the related literature, the taxonomy scheme used for the classification of the 3DSS deep learning methods is ambiguous. Based on the taxonomy schemes of 9 existing review papers, a new taxonomy scheme of the 3DSS deep learning methods is proposed, aiming to standardize it and improve the comparability and clarity across related studies. Furthermore, an extensive overview of the available 3DSS indoor and outdoor datasets is provided along with their links. The core part of the review is the detailed presentation of recent and former 3DSS deep learning methods and their classification using the proposed taxonomy scheme along with their GitHub repositories. Additionally, a brief but informative analysis of the evaluation metrics and loss functions used in 3DSS is included. Finally, a fruitful discussion of the examined 3DSS methods and datasets, is presented to foster new research directions and applications in the field of 3DSS. Supplementary, to this review a GitHub repository is provided (https://github.com/thobet/Deep-Learning-on-3D-Semantic-Segmentation-a- Detailed-Review) including a quick classification of over 400 3DSS methods, using the proposed taxonomy scheme.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 106 of its 171 references that are in the map. First posted on arXiv 4 Nov 2024.

Builds on

106 of 171 references.

References · 106

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38 more
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50 most cited of 106 All 106 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scannerscited by 3
  2. 2026Exploring Easy Boosts for Lidar Semantic Scene Completioncited by 2
  3. 2025White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentationcited by 0
  4. 2025Reproducing and Extending RaDelft 4D Radar with Camera-Assisted Labelscited by 0
  5. 2026Synthetic LiDAR Data Generation and Deterministic Downsampling for Point Cloud Classification on the Edgecited by 0
  6. 2026GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Liftingcited by 0