系统梳理3D语义分割深度学习方法，提供标准化分类与资源库。
Deep Learning on 3D Semantic Segmentation: A Detailed Review
- 提出新分类体系，统一3D语义分割方法的命名与归类标准。
- 整理400+方法、10+数据集及对应代码链接，支持快速检索。
- 适合研究者快速了解领域全貌，推动3D场景理解研究发展。
本文对三维语义分割（3DSS）领域的深度学习方法进行了详尽综述与分析。现有文献中3DSS方法的分类体系模糊不清。基于9篇已有综述的分类方案，本文提出一种新的分类框架，旨在实现标准化，提升不同研究间的可比性与清晰度。此外，全面介绍了当前可用的室内与室外3DSS数据集，并提供其下载链接。综述核心部分详细呈现了近年及经典3DSS深度学习方法，依据新分类体系进行归类，并附带其GitHub仓库地址。同时简要分析了3DSS中常用的评估指标与损失函数。最后，对所考察的方法与数据集进行深入讨论，以激发该领域的新研究方向与应用。为补充本综述，还提供一个GitHub仓库（https://github.com/thobet/Deep-Learning-on-3D-Semantic-Segmentation-a-Detailed-Review），包含使用新分类体系整理的400余种3DSS方法的快速索引。
原文摘要 · Abstract (English)
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.
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