mlsanityData to 14 Oct 2026

Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion

arXiv 2103.07074 ↗First posted 12 Mar 2021cs.CVcited by 72

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Abstract (arXiv)

Given the prominence of current 3D sensors, a fine-grained analysis on the basic point cloud data is worthy of further investigation. Particularly, real point cloud scenes can intuitively capture complex surroundings in the real world, but due to 3D data's raw nature, it is very challenging for machine perception. In this work, we concentrate on the essential visual task, semantic segmentation, for large-scale point cloud data collected in reality. On the one hand, to reduce the ambiguity in nearby points, we augment their local context by fully utilizing both geometric and semantic features in a bilateral structure. On the other hand, we comprehensively interpret the distinctness of the points from multiple resolutions and represent the feature map following an adaptive fusion method at point-level for accurate semantic segmentation. Further, we provide specific ablation studies and intuitive visualizations to validate our key modules. By comparing with state-of-the-art networks on three different benchmarks, we demonstrate the effectiveness of our network.

Key facts. Cited by 72 papers in the map (data through 14 Oct 2026). Builds on 35 of its 57 references that are in the map. First posted on arXiv 12 Mar 2021.

Builds on

35 of 57 references.

References · 35

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
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  4. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
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  6. 2017Squeeze-and-Excitation Networkscited by 4,789
  7. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  8. 2016Pyramid Scene Parsing Networkcited by 2,479
  9. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
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  11. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,208
  12. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,205
23 more
  1. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  2. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 657
  3. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 585
  4. 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
  5. 2020PointContrast: Unsupervised Pre-training for 3D Point Cloud Understandingcited by 504
  6. 2017Joint 2D-3D-Semantic Data for Indoor Scene Understandingcited by 406
  7. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 385
  8. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
  9. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 275
  10. 2020PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Samplingcited by 239
  11. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  12. 2018Tangent Convolutions for Dense Prediction in 3Dcited by 173
  13. 2019PU-GAN: a Point Cloud Upsampling Adversarial Networkcited by 164
  14. 2017Semantic3D.net: A new Large-scale Point Cloud Classification Benchmarkcited by 158
  15. 2018SPLATNet: Sparse Lattice Networks for Point Cloud Processingcited by 158
  16. 2020Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentationcited by 122
  17. 2019ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statisticscited by 104
  18. 2018Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Networkcited by 100
  19. 2017Deep Projective 3D Semantic Segmentationcited by 81
  20. 2020Dense-Resolution Network for Point Cloud Classification and Segmentationcited by 45
  21. 2019Point Cloud Oversegmentation with Graph-Structured Deep Metric Learningcited by 42
  22. 2020Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentationcited by 26
  23. 2020Self-Prediction for Joint Instance and Semantic Segmentation of Point Cloudscited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 11; 2022: 23; 2023: 22; 2024: 12; 2025: 0; 2026: 4.

72 papers.

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  1. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 436
  2. 2023SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Drivingcited by 75
  3. 2023MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Drivingcited by 68
  4. 2022Contrastive Boundary Learning for Point Cloud Segmentationcited by 56
  5. 2022CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Drivingcited by 56
  6. 2022STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Datasetcited by 53
  7. 2022Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learningcited by 46
  8. 2021PVT: Point-Voxel Transformer for Point Cloud Learningcited by 43
  9. 2021PatchFormer: An Efficient Point Transformer with Patch Attentioncited by 43
  10. 2022Meta Architecture for Point Cloud Analysiscited by 40
  11. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 39
  12. 2021PU-Transformer: Point Cloud Upsampling Transformercited by 32
38 more
  1. 2022Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregationcited by 23
  2. 2023Clustering based Point Cloud Representation Learning for 3D Analysiscited by 23
  3. 2021Cascaded Cross MLP-Mixer GANs for Cross-View Image Translationcited by 21
  4. 2022VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum Modelingcited by 21
  5. 2023P2C: Self-Supervised Point Cloud Completion from Single Partial Cloudscited by 20
  6. 2024Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentationcited by 15
  7. 2023SVQNet: Sparse Voxel-Adjacent Query Network for 4D Spatio-Temporal LiDAR Semantic Segmentationcited by 14
  8. 2022Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learningcited by 13
  9. 2023TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentationcited by 13
  10. 2022SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentationcited by 11
  11. 2022PointCaM: Cut-and-Mix for Open-Set Point Cloud Learningcited by 11
  12. 2021Investigating Attention Mechanism in 3D Point Cloud Object Detectioncited by 10
  13. 2021PnP-3D: A Plug-and-Play for 3D Point Cloudscited by 10
  14. 2022Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completioncited by 9
  15. 2024PerLA: Perceptive 3D Language Assistantcited by 9
  16. 2024SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detectioncited by 8
  17. 2021Continuous Conditional Random Field Convolution for Point Cloud Segmentationcited by 7
  18. 2021Efficient Urban-scale Point Clouds Segmentation with BEV Projectioncited by 6
  19. 2022ONeRF: Unsupervised 3D Object Segmentation from Multiple Viewscited by 6
  20. 2023Retro-FPN: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentationcited by 6
  21. 2024Deep Learning on 3D Semantic Segmentation: A Detailed Reviewcited by 6
  22. 2022LACV-Net: Semantic Segmentation of Large-Scale Point Cloud Scene via Local Adaptive and Comprehensive VLADcited by 5
  23. 2023Learning from Mistakes: Self-Regularizing Hierarchical Representations in Point Cloud Semantic Segmentationcited by 5
  24. 2023Full Point Encoding for Local Feature Aggregation in 3D Point Cloudscited by 5
  25. 2023Robust Backdoor Attacks on Object Detection in Real Worldcited by 5
  26. 2022Projection-based Point Convolution for Efficient Point Cloud Segmentationcited by 4
  27. 2022Upsampling Autoencoder for Self-Supervised Point Cloud Learningcited by 4
  28. 20233D Tooth Mesh Segmentation with Simplified Mesh Cell Representationcited by 4
  29. 2023Collect-and-Distribute Transformer for 3D Point Cloud Analysiscited by 4
  30. 2022Variational Distillation for Multi-View Learningcited by 3
  31. 2022PCB-RandNet: Rethinking Random Sampling for LIDAR Semantic Segmentation in Autonomous Driving Scenecited by 3
  32. 2022PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloudcited by 3
  33. 2023GAM : Gradient Attention Module of Optimization for Point Clouds Analysiscited by 3
  34. 2023Small but Mighty: Enhancing 3D Point Clouds Semantic Segmentation with U-Next Frameworkcited by 3
  35. 2022Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental Modelcited by 2
  36. 2023Viewport Prediction for Volumetric Video Streaming by Exploring Video Saliency and Trajectory Informationcited by 2
  37. 2021Two Heads are Better than One: Geometric-Latent Attention for Point Cloud Classification and Segmentationcited by 1
  38. 2021Neighborhood Spatial Aggregation MC Dropout for Efficient Uncertainty-aware Semantic Segmentation in Point Cloudscited by 1

50 most cited of 72 All 72 (JSON)