Open3DML

Open3DML is an open-source library within the Open3D ecosystem that provides tools for developing and evaluating machine learning models on 3D data. It is sometimes referred to as Open3D-ML and extends the core Open3D framework with components tailored for 3D deep learning tasks.

The library offers data structures and utilities for handling 3D data, including point clouds, meshes, and voxel grids. It includes dataset classes, data loaders, and data augmentation pipelines designed to support common 3D machine learning workflows. Open3DML also provides reference model implementations and training scripts for tasks such as 3D semantic segmentation, object classification, and 3D registration and pose estimation.

Open3DML is designed to work with popular deep learning frameworks, providing interfaces compatible with PyTorch and TensorFlow. It integrates with Open3D’s visualization and geometric processing tools to facilitate end-to-end pipelines from data loading and preprocessing to model training and result visualization. The library emphasizes modularity, enabling researchers and developers to experiment with different network architectures and loss functions on standard 3D benchmarks.

Typical workflows involve installing the package, preparing 3D datasets, selecting or implementing a model in a supported framework, training, evaluating using standardized metrics, and visualizing results. Open3DML serves both research and development settings, offering tutorials and documentation to guide users through common tasks in 3D deep learning.

Open3DML is maintained as part of the Open3D project with contributions from its user community. It is intended to complement Open3D’s core processing and visualization capabilities by providing a robust foundation for machine learning on 3D data.