  We introduce Torch-Points3D, an open-source framework designed to facilitate
the use of deep networks on3D data. Its modular design, efficient
implementation, and user-friendly interfaces make it a relevant tool for
research and productization alike. Beyond multiple quality-of-life features,
our goal is to standardize a higher level of transparency and reproducibility
in 3D deep learning research, and to lower its barrier to entry. In this paper,
we present the design principles of Torch-Points3D, as well as extensive
benchmarks of multiple state-of-the-art algorithms and inference schemes across
several datasets and tasks. The modularity of Torch-Points3D allows us to
design fair and rigorous experimental protocols in which all methods are
evaluated in the same conditions. The Torch-Points3D repository
:https://github.com/nicolas-chaulet/torch-points3d
