# Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning

arXiv: https://arxiv.org/abs/1809.00426 · HTML: https://mlsanity.com/paper/1809.00426/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2018.

## Built on by (most cited first, 19 of 19)

- [LaserMix for Semi-Supervised LiDAR Semantic Segmentation](https://mlsanity.com/paper/2207.00026/) (2022)
- [One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation](https://mlsanity.com/paper/2104.02246/) (2021)
- [SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network](https://mlsanity.com/paper/2104.07861/) (2021)
- [Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving](https://mlsanity.com/paper/2405.05258/) (2024)
- [STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset](https://mlsanity.com/paper/2203.09065/) (2022)
- [LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds](https://mlsanity.com/paper/2210.08064/) (2022)
- [Multimodal Semi-Supervised Learning for 3D Objects](https://mlsanity.com/paper/2110.11601/) (2021)
- [MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding](https://mlsanity.com/paper/2107.00346/) (2021)
- [Superpoint-guided Semi-supervised Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/2107.03601/) (2021)
- [Point Cloud Instance Segmentation with Semi-supervised Bounding-Box Mining](https://mlsanity.com/paper/2111.15210/) (2021)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2301.13361/) (2023)
- [A Cross-Scale Hierarchical Transformer with Correspondence-Augmented Attention for inferring Bird's-Eye-View Semantic Segmentation](https://mlsanity.com/paper/2304.03650/) (2023)
- [VIN: Voxel-based Implicit Network for Joint 3D Object Detection and Segmentation for Lidars](https://mlsanity.com/paper/2107.02980/) (2021)
- [SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation](https://mlsanity.com/paper/2407.12788/) (2024)
- [You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning](https://mlsanity.com/paper/2211.09302/) (2022)
- [You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding](https://mlsanity.com/paper/2303.14727/) (2023)
- [A Spatiotemporal Correspondence Approach to Unsupervised LiDAR Segmentation with Traffic Applications](https://mlsanity.com/paper/2308.12433/) (2023)
- [DSOcc: Leveraging Depth Awareness and Semantic Aid to Boost Camera-Based 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2505.20951/) (2025)

## Abstract (arXiv)

This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications. A system of semantic segmentation using 3D LiDAR data, including range image segmentation, sample generation, inter-frame data association, track-level annotation and semi-supervised learning, is developed. To reduce the considerable requirement of fine annotations, a CNN-based classifier is trained by considering both supervised samples with manually labeled object classes and pairwise constraints, where a data sample is composed of a segment as the foreground and neighborhood points as the background. A special loss function is designed to account for both annotations and constraints, where the constraint data are encouraged to be assigned to the same semantic class. A dataset containing 1838 frames of LiDAR data, 39934 pairwise constraints and 57927 human annotations is developed. The performance of the method is examined extensively. Qualitative and quantitative experiments show that the combination of a few annotations and large amount of constraint data significantly enhances the effectiveness and scene adaptability, resulting in greater than 10% improvement