统一3D点云学习库，支持分类、分割与自监督预训练
LIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learning
- 基于注册表框架整合55个模型配置，覆盖29种主干网络
- 提供标准化训练流程与统计验证工具，支持公平多模型对比
- 适合研究者快速实验，尤其擅长少样本学习与自监督任务
三维点云分析在自动驾驶、机器人、林业及生态监测等领域日益重要。尽管已有大量深度学习方法用于点云理解，包括监督主干网络、自监督预训练（SSL）和参数高效微调（PEFT），但其代码分散于不兼容的仓库中，数据管道、评估协议和配置格式各异，难以进行公平比较。我们提出 \\(lib\\)，一个统一、可扩展的PyTorch库，集成超过55个模型配置，涵盖29种监督架构、7种自监督预训练方法和5种参数高效微调策略，均在单一注册表框架内支持分类、语义分割、部件分割和少样本学习。该库提供标准化训练运行器、分层K折交叉验证、自动LaTeX/CSV表格生成、内置Friedman/Nemenyi统计检验及临界差异图，实现严格的多模型对比，并配备2200+自动化测试用例，端到端验证每个配置。代码开源于 https://github.com/said-ohamouddou/LIDARLearn，采用MIT许可。
原文摘要 · Abstract (English)
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.
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