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.

翻译：本文对三维语义分割领域近期及早期的深度学习方法进行了详尽综述与综合分析。现有相关文献中，用于三维语义分割深度学习方法的分类体系存在模糊性。基于九篇现有综述论文的分类框架，本文提出了一种新的三维语义分割深度学习方法分类体系，旨在实现标准化并提升相关研究的可比性与清晰度。此外，本文系统梳理了当前可用的室内外三维语义分割数据集及其访问链接。本综述的核心内容是通过提出的分类体系，对近期及早期的三维语义分割深度学习方法及其GitHub代码库进行详细阐述与归类。同时，文中还包含对三维语义分割常用评估指标与损失函数的简要分析。最后，通过对现有三维语义分割方法与数据集的深入讨论，为领域内新的研究方向与应用拓展提供启示。作为本综述的补充，我们同步开放GitHub代码库（https://github.com/thobet/Deep-Learning-on-3D-Semantic-Segmentation-a-Detailed-Review），其中采用提出的分类体系对400余种三维语义分割方法进行了快速分类。