# Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation

Source: [Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation](<https://www.sota2.com/research/paper/68fa67d31e209131cea18b6b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:23:05.784Z

Authors: Yuxing Xie, Jiaojiao Tian, Xiao Xiang Zhu

Published: 2019-08-23T14:55:11.000Z

## Abstract

3D Point Cloud Semantic Segmentation (PCSS) is attracting increasing interest, due to its applicability in remote sensing, computer vision and robotics, and due to the new possibilities offered by deep learning techniques. In order to provide a needed up-to-date review of recent developments in PCSS, this article summarizes existing studies on this topic. Firstly, we outline the acquisition and evolution of the 3D point cloud from the perspective of remote sensing and computer vision, as well as the published benchmarks for PCSS studies. Then, traditional and advanced techniques used for Point Cloud Segmentation (PCS) and PCSS are reviewed and compared. Finally, important issues and open questions in PCSS studies are discussed.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1908.08854>)

[PDF](<https://arxiv.org/pdf/1908.08854.pdf>)