# PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud

arXiv: https://arxiv.org/abs/1807.06288 · HTML: https://mlsanity.com/paper/1807.06288/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

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

- [MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation](https://mlsanity.com/paper/2204.12667/) (2022)
- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [Open-world Semantic Segmentation for LIDAR Point Clouds](https://mlsanity.com/paper/2207.01452/) (2022)
- [CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation](https://mlsanity.com/paper/2204.09914/) (2022)
- [FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation](https://mlsanity.com/paper/2103.00738/) (2021)
- [An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models](https://mlsanity.com/paper/2405.14870/) (2024)
- [RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds](https://mlsanity.com/paper/2205.01570/) (2022)
- [Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation](https://mlsanity.com/paper/2310.20293/) (2023)
- [Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Data](https://mlsanity.com/paper/2203.00214/) (2022)
- [Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving Applications](https://mlsanity.com/paper/2203.01087/) (2022)
- [3D Adversarial Augmentations for Robust Out-of-Domain Predictions](https://mlsanity.com/paper/2308.15479/) (2023)
- [SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics](https://mlsanity.com/paper/2403.06501/) (2024)
- [Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulation](https://mlsanity.com/paper/2404.15774/) (2024)
- [Augmented Efficiency: Reducing Memory Footprint and Accelerating Inference for 3D Semantic Segmentation through Hybrid Vision](https://mlsanity.com/paper/2407.16102/) (2024)
- [Weighted Bayesian Gaussian Mixture Model for Roadside LiDAR Object Detection](https://mlsanity.com/paper/2204.09804/) (2022)
- [A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2207.05888/) (2022)
- [Optimizing Sparse Convolution on GPUs with CUDA for 3D Point Cloud Processing in Embedded Systems](https://mlsanity.com/paper/2402.07710/) (2024)
- [The 2nd Place Solution from the 3D Semantic Segmentation Track in the 2024 Waymo Open Dataset Challenge](https://mlsanity.com/paper/2501.05472/) (2025)
- [ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions](https://mlsanity.com/paper/2604.10213/) (2026)

## Abstract (arXiv)

In this paper, we propose PointSeg, a real-time end-to-end semantic segmentation method for road-objects based on spherical images. We take the spherical image, which is transformed from the 3D LiDAR point clouds, as input of the convolutional neural networks (CNNs) to predict the point-wise semantic map. To make PointSeg applicable on a mobile system, we build the model based on the light-weight network, SqueezeNet, with several improvements. It maintains a good balance between memory cost and prediction performance. Our model is trained on spherical images and label masks projected from the KITTI 3D object detection dataset. Experiments show that PointSeg can achieve competitive accuracy with 90fps on a single GPU 1080ti. which makes it quite compatible for autonomous driving applications.