mlsanityData to 7 Oct 2026

PCSCNet: Fast 3D Semantic Segmentation of LiDAR Point Cloud for Autonomous Car using Point Convolution and Sparse Convolution Network

arXiv 2202.10047 ↗First posted 21 Feb 2022cs.CVcited by 14

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Abstract (arXiv)

The autonomous car must recognize the driving environment quickly for safe driving. As the Light Detection And Range (LiDAR) sensor is widely used in the autonomous car, fast semantic segmentation of LiDAR point cloud, which is the point-wise classification of the point cloud within the sensor framerate, has attracted attention in recognition of the driving environment. Although the voxel and fusion-based semantic segmentation models are the state-of-the-art model in point cloud semantic segmentation recently, their real-time performance suffer from high computational load due to high voxel resolution. In this paper, we propose the fast voxel-based semantic segmentation model using Point Convolution and 3D Sparse Convolution (PCSCNet). The proposed model is designed to outperform at both high and low voxel resolution using point convolution-based feature extraction. Moreover, the proposed model accelerates the feature propagation using 3D sparse convolution after the feature extraction. The experimental results demonstrate that the proposed model outperforms the state-of-the-art real-time models in semantic segmentation of SemanticKITTI and nuScenes, and achieves the real-time performance in LiDAR point cloud inference.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 26 of its 36 references that are in the map. First posted on arXiv 21 Feb 2022.

Builds on

26 of 36 references.

References · 26

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  4. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  5. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  6. 2020Searching Efficient 3D Architectures with Sparse Point-Voxel Convolutioncited by 422
  7. 2020Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentationcited by 388
  8. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 340
  9. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 274
  10. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 231
  11. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  12. 2020SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Drivingcited by 226
14 more
  1. 2017Submanifold Sparse Convolutional Networkscited by 223
  2. 2020Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completioncited by 222
  3. 2021RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentationcited by 173
  4. 2020SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentationcited by 170
  5. 2021(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Networkcited by 164
  6. 2021Deep Parametric Continuous Convolutional Neural Networkscited by 161
  7. 2017Pointwise Convolutional Neural Networkscited by 126
  8. 2019ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statisticscited by 104
  9. 2020AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentationcited by 87
  10. 2018Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Cloudscited by 62
  11. 2021Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutionscited by 37
  12. 2020Depth Based Semantic Scene Completion with Position Importance Aware Losscited by 36
  13. 2014Spatially-sparse convolutional neural networkscited by 33
  14. 2021FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentationcited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 5; 2026: 2.

14 papers.

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  1. 2023Rethinking Range View Representation for LiDAR Segmentationcited by 126
  2. 2023Using a Waffle Iron for Automotive Point Cloud Semantic Segmentationcited by 53
  3. 2023FRNet: Frustum-Range Networks for Scalable LiDAR Segmentationcited by 50
  4. 2024SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Cloudscited by 13
  5. 2024Exploiting Local Features and Range Images for Small Data Real-Time Point Cloud Semantic Segmentationcited by 4
  6. 2024Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Surveycited by 3
  7. 2025FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentationcited by 3
  8. 2025You Only Click Once: Single Point Weakly Supervised 3D Instance Segmentation for Autonomous Drivingcited by 1
  9. 2025Road Boundary Detection Using 4D mmWave Radar for Autonomous Drivingcited by 1
  10. 20243D Landmark Detection on Human Point Clouds: A Benchmark and A Dual Cascade Point Transformer Frameworkcited by 0
  11. 2025Multimodal Point Cloud Semantic Segmentation With Virtual Point Enhancementcited by 0
  12. 2025DAGLFNet: Deep Feature Attention Guided Global and Local Feature Fusion for Pseudo-Image Point Cloud Segmentationcited by 0
2 more
  1. 2026Vanilla ViT for Automotive Point Cloud Semantic Segmentationcited by 0
  2. 2026CDSeg: A Renderable Gaussian Carrier for Image-to-3D Label Transfercited by 0