LIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learning
Abstract
A unified PyTorch library for 3D point cloud analysis that standardizes model implementations, training protocols, and evaluation methods across multiple deep learning approaches.
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 , 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. 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.
Community
We’re proud to open-source LIDARLearn 🎉
It’s a unified PyTorch library for 3D point cloud deep learning. To our knowledge, it’s the first framework that supports such a large collection of models in one place, with built-in cross-validation support.
It brings together 56 ready-to-use configurations covering supervised, self-supervised, and parameter-efficient fine-tuning methods.
You can run everything from a single YAML file with one simple command.
One of the best features: after training, you can automatically generate a publication-ready LaTeX PDF. It creates clean tables, highlights the best results, and runs statistical tests and diagrams for you. No need to build tables manually in Overleaf.
The library includes benchmarks on datasets like ModelNet40, ShapeNet, S3DIS, and two remote sensing datasets (STPCTLS and HELIALS). STPCTLS is already preprocessed, so you can use it right away.
This project is intended for researchers in 3D point cloud learning, 3D computer vision, and remote sensing.
It’s released under the MIT license.
Contributions and benchmarks are welcome!
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