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

arXiv: https://arxiv.org/abs/2205.01570 · HTML: https://mlsanity.com/paper/2205.01570/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 29 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://mlsanity.com/paper/1902.06326/) (2019)
- [SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud](https://mlsanity.com/paper/1710.07368/) (2017)
- [SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud](https://mlsanity.com/paper/1809.08495/) (2018)
- [Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates](https://mlsanity.com/paper/1708.07120/) (2017)
- [LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving](https://mlsanity.com/paper/1903.08701/) (2019)
- [PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud](https://mlsanity.com/paper/1807.06288/) (2018)
- [RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud](https://mlsanity.com/paper/1905.08748/) (2019)

## Built on by (most cited first, 11 of 11)

- [Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space](https://mlsanity.com/paper/2303.04599/) (2023)
- [Rethinking LiDAR Domain Generalization: Single Source as Multiple Density Domains](https://mlsanity.com/paper/2312.12098/) (2023)
- [Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2302.08785/) (2023)
- [ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data](https://mlsanity.com/paper/2303.04351/) (2023)
- [SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection](https://mlsanity.com/paper/2308.12863/) (2023)
- [Active Inference-Based Optimization of Discriminative Neural Network Classifiers](https://mlsanity.com/paper/2306.02447/) (2023)
- [Point2Point : A Framework for Efficient Deep Learning on Hilbert sorted Point Clouds with applications in Spatio-Temporal Occupancy Prediction](https://mlsanity.com/paper/2306.16306/) (2023)
- [R2Det: Redemption from Range-view for Accurate 3D Object Detection](https://mlsanity.com/paper/2307.11482/) (2023)
- [Lidar Annotation Is All You Need](https://mlsanity.com/paper/2311.04777/) (2023)
- [RangeSAM: On the Potential of Visual Foundation Models for Range-View represented LiDAR segmentation](https://mlsanity.com/paper/2509.15886/) (2025)
- [HyperLiDAR: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computing](https://mlsanity.com/paper/2604.12331/) (2026)

## 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.