mlsanityData to 7 Oct 2026

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

arXiv 1807.06288 ↗First posted 17 Jul 2018cs.CVcited by 19

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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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jul 2018.

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Citing papers per year: 2021: 2; 2022: 8; 2023: 2; 2024: 5; 2025: 1; 2026: 1.

19 papers.

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  1. 2022MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentationcited by 76
  2. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
  3. 2022Open-world Semantic Segmentation for LIDAR Point Cloudscited by 29
  4. 2022CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentationcited by 15
  5. 2021FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentationcited by 14
  6. 2024An Empirical Study of Training State-of-the-Art LiDAR Segmentation Modelscited by 12
  7. 2022RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Cloudscited by 11
  8. 2023Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentationcited by 11
  9. 2022Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Datacited by 4
  10. 2022Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving Applicationscited by 4
  11. 20233D Adversarial Augmentations for Robust Out-of-Domain Predictionscited by 4
  12. 2024SeSame: Simple, Easy 3D Object Detection with Point-Wise Semanticscited by 3
7 more
  1. 2024Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulationcited by 1
  2. 2024Augmented Efficiency: Reducing Memory Footprint and Accelerating Inference for 3D Semantic Segmentation through Hybrid Visioncited by 1
  3. 2022Weighted Bayesian Gaussian Mixture Model for Roadside LiDAR Object Detectioncited by 0
  4. 2022A Near Sensor Edge Computing System for Point Cloud Semantic Segmentationcited by 0
  5. 2024Optimizing Sparse Convolution on GPUs with CUDA for 3D Point Cloud Processing in Embedded Systemscited by 0
  6. 2025The 2nd Place Solution from the 3D Semantic Segmentation Track in the 2024 Waymo Open Dataset Challengecited by 0
  7. 2026ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditionscited by 0