mlsanityData to 7 Oct 2026

RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds

arXiv 2205.01570 ↗First posted 2 May 2022cs.CV cs.LGcited by 11

Not on the map. Why?

Abstract (arXiv)

Semantic outdoor scene understanding based on 3D LiDAR point clouds is a challenging task for autonomous driving due to the sparse and irregular data structure. This paper takes advantages of the uneven range distribution of different LiDAR laser beams to propose a range aware instance segmentation network, RangeSeg. RangeSeg uses a shared encoder backbone with two range dependent decoders. A heavy decoder only computes top of a range image where the far and small objects locate to improve small object detection accuracy, and a light decoder computes whole range image for low computational cost. The results are further clustered by the DBSCAN method with a resolution weighted distance function to get instance-level segmentation results. Experiments on the KITTI dataset show that RangeSeg outperforms the state-of-the-art semantic segmentation methods with enormous speedup and improves the instance-level segmentation performance on small and far objects. The whole RangeSeg pipeline meets the real time requirement on NVIDIAtextsuperscripttextregistered JETSON AGX Xavier with 19 frames per second in average.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 20 of its 29 references that are in the map. First posted on arXiv 2 May 2022.

Builds on

20 of 29 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  4. 2017Squeeze-and-Excitation Networkscited by 4,763
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  6. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  7. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  8. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  9. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
  10. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  11. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  12. 2017Deep Layer Aggregationcited by 436
8 more
  1. 2017Semantic Foggy Scene Understanding with Synthetic Datacited by 401
  2. 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
  3. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 231
  4. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  5. 2017Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Ratescited by 196
  6. 2019LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Drivingcited by 133
  7. 2018PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloudcited by 19
  8. 2019RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloudcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 0; 2025: 1; 2026: 1.

11 papers.

  1. 2023Point Cloud Classification Using Content-based Transformer via Clustering in Feature Spacecited by 19
  2. 2023Rethinking LiDAR Domain Generalization: Single Source as Multiple Density Domainscited by 7
  3. 2023Few-shot 3D LiDAR Semantic Segmentation for Autonomous Drivingcited by 3
  4. 2023ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Datacited by 3
  5. 2023SkipcrossNets: Adaptive Skip-cross Fusion for Road Detectioncited by 3
  6. 2023Active Inference-Based Optimization of Discriminative Neural Network Classifierscited by 0
  7. 2023Point2Point : A Framework for Efficient Deep Learning on Hilbert sorted Point Clouds with applications in Spatio-Temporal Occupancy Predictioncited by 0
  8. 2023R2Det: Redemption from Range-view for Accurate 3D Object Detectioncited by 0
  9. 2023Lidar Annotation Is All You Needcited by 0
  10. 2025RangeSAM: On the Potential of Visual Foundation Models for Range-View represented LiDAR segmentationcited by 0
  11. 2026HyperLiDAR: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computingcited by 0