mlsanityData to 7 Oct 2026

LIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learning

arXiv 2604.10780 ↗First posted 12 Apr 2026cs.CVcited by 0

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Abstract (arXiv)

Three-dimensional (3D) point cloud analysis has become central to applications ranging from autonomous driving and robotics to forestry and ecological monitoring. Although numerous deep learning methods have been proposed for point cloud understanding, including supervised backbones, self-supervised pre-training (SSL), and parameter-efficient fine-tuning (PEFT), their implementations are scattered across incompatible codebases with differing data pipelines, evaluation protocols, and configuration formats, making fair comparisons difficult. We introduce lib, a unified, extensible PyTorch library that integrates over 55 model configurations covering 29 supervised architectures, seven SSL pre-training methods, and five PEFT strategies, all within a single registry-based framework supporting classification, semantic segmentation, part segmentation, and few-shot learning. lib provides standardised training runners, cross-validation with stratified K-fold splitting, automated LaTeX/CSV table generation, built-in Friedman/Nemenyi statistical testing with critical-difference diagrams for rigorous multi-model comparison, and a comprehensive test suite with 2,200+ automated tests validating every configuration end-to-end. The code is available at https://github.com/said-ohamouddou/LIDARLearn under the MIT licence.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 36 of its 40 references that are in the map. First posted on arXiv 12 Apr 2026.

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36 of 40 references.

References · 36

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  4. 2022Visual Prompt Tuningcited by 1,674
  5. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  6. 2020PCT: Point cloud transformercited by 730
  7. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  8. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 647
  9. 2023Point Transformer V3: Simpler, Faster, Strongercited by 579
  10. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  11. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 456
  12. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 384
24 more
  1. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 376
  2. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
  3. 2022Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-trainingcited by 263
  4. 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
  5. 2018PPFNet: Global Context Aware Local Features for Robust 3D Point Matchingcited by 180
  6. 2021Walk in the Cloud: Learning Curves for Point Clouds Shape Analysiscited by 172
  7. 2023Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretrainingcited by 162
  8. 2022Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?cited by 121
  9. 2023PointGPT: Auto-regressively Generative Pre-training from Point Cloudscited by 115
  10. 2022Surface Representation for Point Cloudscited by 84
  11. 2022P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Promptingcited by 74
  12. 2020Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloudcited by 71
  13. 2023Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Modelscited by 67
  14. 2024Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysiscited by 52
  15. 2024Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learningcited by 43
  16. 2021PVT: Point-Voxel Transformer for Point Cloud Learningcited by 42
  17. 2024PCP-MAE: Learning to Predict Centers for Point Masked Autoencoderscited by 23
  18. 2024Parameter-efficient Prompt Learning for 3D Point Cloud Understandingcited by 16
  19. 2024PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point Setscited by 15
  20. 2023Decoupled Local Aggregation for Point Cloud Learningcited by 12
  21. 2022Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognitioncited by 9
  22. 2022PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Samplingcited by 4
  23. 2025Introducing the Short-Time Fourier Kolmogorov Arnold Network: A Dynamic Graph CNN Approach for Tree Species Classification in 3D Point Cloudscited by 4
  24. 2025MS-DGCNN++: Multi-Scale Dynamic Graph Convolution with Scale-Dependent Normalization for Robust LiDAR Tree Species Classificationcited by 1

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