论文标题
火炬点3D：一个模块化的多任务框架，用于3D点云上可重现的深度学习
Torch-Points3D: A Modular Multi-Task Frameworkfor Reproducible Deep Learning on 3D Point Clouds
论文作者
论文摘要
我们介绍了Torch-Points3D，这是一个开源框架，旨在促进深网的使用3D数据。它的模块化设计，有效的实现和用户友好的接口使其成为研究和生产的相关工具。除了多种生活质量功能之外，我们的目标是在3D深度学习研究中标准化更高水平的透明度和可重复性，并降低其进入障碍。在本文中，我们介绍了Torch-Points3D的设计原理，以及在几个数据集和任务中的多种最新算法和推理方案的广泛基准。火炬点3D的模块化使我们能够设计公平而严格的实验协议，其中在相同条件下评估所有方法。 Torch-Points3D存储库：https：//github.com/nicolas-chaulet/torch-points3d
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
