# MaskRange: A Mask-classification Model for Range-view based LiDAR Segmentation

arXiv: https://arxiv.org/abs/2206.12073 · HTML: https://mlsanity.com/paper/2206.12073/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 58 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 30 of 46)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [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)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution](https://mlsanity.com/paper/2007.16100/) (2020)
- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation](https://mlsanity.com/paper/2011.10033/) (2020)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving](https://mlsanity.com/paper/2003.03653/) (2020)
- [Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion](https://mlsanity.com/paper/2012.03762/) (2020)
- [K-Net: Towards Unified Image Segmentation](https://mlsanity.com/paper/2106.14855/) (2021)
- [RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation](https://mlsanity.com/paper/2103.12978/) (2021)
- [SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation](https://mlsanity.com/paper/2004.01803/) (2020)
- [Mix3D: Out-of-Context Data Augmentation for 3D Scenes](https://mlsanity.com/paper/2110.02210/) (2021)
- [Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review](https://mlsanity.com/paper/2005.09830/) (2020)
- [Seamless Scene Segmentation](https://mlsanity.com/paper/1905.01220/) (2019)
- [Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers](https://mlsanity.com/paper/2109.03814/) (2021)

## Built on by (most cited first, 15 of 15)

- [Rethinking Range View Representation for LiDAR Segmentation](https://mlsanity.com/paper/2303.05367/) (2023)
- [FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation](https://mlsanity.com/paper/2312.04484/) (2023)
- [Multi-Space Alignments Towards Universal LiDAR Segmentation](https://mlsanity.com/paper/2405.01538/) (2024)
- [How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation](https://mlsanity.com/paper/2505.18956/) (2025)
- [Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning](https://mlsanity.com/paper/2506.13265/) (2025)
- [UP-Fuse: Uncertainty-guided LiDAR-Camera Fusion for 3D Panoptic Segmentation](https://mlsanity.com/paper/2602.19349/) (2026)
- [CurveCloudNet: Processing Point Clouds with 1D Structure](https://mlsanity.com/paper/2303.12050/) (2023)
- [FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation](https://mlsanity.com/paper/2502.09274/) (2025)
- [PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation](https://mlsanity.com/paper/2306.15348/) (2023)
- [MaskBEV: Joint Object Detection and Footprint Completion for Bird's-eye View 3D Point Clouds](https://mlsanity.com/paper/2307.01864/) (2023)
- [PC-BEV: An Efficient Polar-Cartesian BEV Fusion Framework for LiDAR Semantic Segmentation](https://mlsanity.com/paper/2412.14821/) (2024)
- [Is clustering enough for LiDAR instance segmentation? A state-of-the-art training-free baseline](https://mlsanity.com/paper/2503.13203/) (2025)
- [DQFormer: Towards Unified LiDAR Panoptic Segmentation with Decoupled Queries](https://mlsanity.com/paper/2408.15813/) (2024)
- [RangeSAM: On the Potential of Visual Foundation Models for Range-View represented LiDAR segmentation](https://mlsanity.com/paper/2509.15886/) (2025)
- [PanDA: Unsupervised Domain Adaptation for Multimodal 3D Panoptic Segmentation in Autonomous Driving](https://mlsanity.com/paper/2604.19379/) (2026)

## Abstract (arXiv)

Range-view based LiDAR segmentation methods are attractive for practical applications due to their direct inheritance from efficient 2D CNN architectures. In literature, most range-view based methods follow the per-pixel classification paradigm. Recently, in the image segmentation domain, another paradigm formulates segmentation as a mask-classification problem and has achieved remarkable performance. This raises an interesting question: can the mask-classification paradigm benefit the range-view based LiDAR segmentation and achieve better performance than the counterpart per-pixel paradigm? To answer this question, we propose a unified mask-classification model, MaskRange, for the range-view based LiDAR semantic and panoptic segmentation. Along with the new paradigm, we also propose a novel data augmentation method to deal with overfitting, context-reliance, and class-imbalance problems. Extensive experiments are conducted on the SemanticKITTI benchmark. Among all published range-view based methods, our MaskRange achieves state-of-the-art performance with 66.10 mIoU on semantic segmentation and promising results with 53.10 PQ on panoptic segmentation with high efficiency. Our code will be released.