mlsanityData to 14 Oct 2026

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

arXiv 2206.12073 ↗First posted 24 Jun 2022cs.CVcited by 15

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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.

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

46 of 58 references.

References · 46

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34 more
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Cited by year (2026 to 14 Oct)

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

15 papers.

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  1. 2023Rethinking Range View Representation for LiDAR Segmentationcited by 128
  2. 2023FRNet: Frustum-Range Networks for Scalable LiDAR Segmentationcited by 50
  3. 2024Multi-Space Alignments Towards Universal LiDAR Segmentationcited by 25
  4. 2025How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentationcited by 8
  5. 2025Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learningcited by 6
  6. 2026UP-Fuse: Uncertainty-guided LiDAR-Camera Fusion for 3D Panoptic Segmentationcited by 6
  7. 2023CurveCloudNet: Processing Point Clouds with 1D Structurecited by 5
  8. 2025FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentationcited by 3
  9. 2023PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregationcited by 2
  10. 2023MaskBEV: Joint Object Detection and Footprint Completion for Bird's-eye View 3D Point Cloudscited by 2
  11. 2024PC-BEV: An Efficient Polar-Cartesian BEV Fusion Framework for LiDAR Semantic Segmentationcited by 2
  12. 2025Is clustering enough for LiDAR instance segmentation? A state-of-the-art training-free baselinecited by 1
3 more
  1. 2024DQFormer: Towards Unified LiDAR Panoptic Segmentation with Decoupled Queriescited by 0
  2. 2025RangeSAM: On the Potential of Visual Foundation Models for Range-View represented LiDAR segmentationcited by 0
  3. 2026PanDA: Unsupervised Domain Adaptation for Multimodal 3D Panoptic Segmentation in Autonomous Drivingcited by 0