mlsanityData to 14 Oct 2026

TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation

arXiv 2309.07849 ↗First posted 14 Sep 2023cs.CVcited by 13

Not on the map. Why?

Abstract (arXiv)

LiDAR semantic segmentation plays a crucial role in enabling autonomous driving and robots to understand their surroundings accurately and robustly. A multitude of methods exist within this domain, including point-based, range-image-based, polar-coordinate-based, and hybrid strategies. Among these, range-image-based techniques have gained widespread adoption in practical applications due to their efficiency. However, they face a significant challenge known as the ``many-to-one'' problem caused by the range image's limited horizontal and vertical angular resolution. As a result, around 20% of the 3D points can be occluded. In this paper, we present TFNet, a range-image-based LiDAR semantic segmentation method that utilizes temporal information to address this issue. Specifically, we incorporate a temporal fusion layer to extract useful information from previous scans and integrate it with the current scan. We then design a max-voting-based post-processing technique to correct false predictions, particularly those caused by the ``many-to-one'' issue. We evaluated the approach on two benchmarks and demonstrated that the plug-in post-processing technique is generic and can be applied to various networks.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 47 of its 56 references that are in the map. First posted on arXiv 14 Sep 2023.

Builds on

47 of 56 references.

References · 47

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  4. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  6. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  7. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,208
  8. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,205
  9. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 585
  10. 2020Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentationcited by 389
  11. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  12. 2022BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervisioncited by 302
35 more
  1. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 275
  2. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 232
  3. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  4. 2020SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Drivingcited by 226
  5. 2022UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learningcited by 184
  6. 2020SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentationcited by 170
  7. 20222DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Cloudscited by 158
  8. 2023Rethinking Range View Representation for LiDAR Segmentationcited by 128
  9. 2023Segment Any Point Cloud Sequences by Distilling Vision Foundation Modelscited by 125
  10. 2020SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instancescited by 123
  11. 2021SuMa++: Efficient LiDAR-based Semantic SLAMcited by 100
  12. 2023SCPNet: Semantic Scene Completion on Point Cloudcited by 80
  13. 2023RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Drivingcited by 76
  14. 2021Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusioncited by 72
  15. 2021OverlapNet: Loop Closing for LiDAR-based SLAMcited by 72
  16. 2022OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognitioncited by 69
  17. 2021Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Datacited by 64
  18. 2022CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Drivingcited by 56
  19. 2021Temporal Memory Attention for Video Semantic Segmentationcited by 50
  20. 2020KPRNet: Improving projection-based LiDAR semantic segmentationcited by 47
  21. 2021FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decodingcited by 46
  22. 2020Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platformcited by 40
  23. 2021Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutionscited by 37
  24. 2022GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentationcited by 36
  25. 2022Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentationcited by 29
  26. 2019Boundary Loss for Remote Sensing Imagery Semantic Segmentationcited by 27
  27. 2022Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregationcited by 23
  28. 2022SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Datacited by 20
  29. 2023MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Cloudscited by 19
  30. 2021PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillarscited by 18
  31. 2023Synchronize Feature Extracting and Matching: A Single Branch Framework for 3D Object Trackingcited by 16
  32. 2022CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentationcited by 15
  33. 2023Correlation Pyramid Network for 3D Single Object Trackingcited by 10
  34. 2022Analyzing Deep Learning Representations of Point Clouds for Real-Time In-Vehicle LiDAR Perceptioncited by 8
  35. 2023CMDFusion: Bidirectional Fusion Network with Cross-modality Knowledge Distillation for LIDAR Semantic Segmentationcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 4; 2026: 3.

13 papers.

Sort:

By area:

  1. 2024SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Groundingcited by 67
  2. 2024VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecastingcited by 35
  3. 2024Is Your LiDAR Placement Optimized for 3D Scene Understanding?cited by 26
  4. 2024An Empirical Study of Training State-of-the-Art LiDAR Segmentation Modelscited by 12
  5. 2025Future-Aware Interaction Network For Motion Forecastingcited by 4
  6. 2024StreamMOS: Streaming Moving Object Segmentation with Multi-View Perception and Dual-Span Memorycited by 3
  7. 2025FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentationcited by 3
  8. 2025Global-Aware Monocular Semantic Scene Completion with State Space Modelscited by 3
  9. 2025Zero-Shot 3D Visual Grounding from Vision-Language Modelscited by 2
  10. 2024OneNet: A Channel-Wise 1D Convolutional U-Netcited by 0
  11. 2026PFGNet: A Fully Convolutional Frequency-Guided Peripheral Gating Network for Efficient Spatiotemporal Predictive Learningcited by 0
  12. 2026HyperLiDAR: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computingcited by 0
1 more
  1. 2026LOGOS: LiDAR-Only Gaussian Elevation Splatting for Unified Tiny Obstacle Segmentationcited by 0