In the rapidly evolving field of autonomous driving, precise segmentation of
LiDAR data is crucial for understanding complex 3D environments. Traditional
approaches often rely on disparate, standalone codebases, hindering unified
advancements and fair benchmarking across models. To address these challenges,
we introduce MMDetection3D-lidarseg, a comprehensive toolbox designed for the
efficient training and evaluation of state-of-the-art LiDAR segmentation
models. We support a wide range of segmentation models and integrate advanced
data augmentation techniques to enhance robustness and generalization.
Additionally, the toolbox provides support for multiple leading sparse
convolution backends, optimizing computational efficiency and performance. By
fostering a unified framework, MMDetection3D-lidarseg streamlines development
and benchmarking, setting new standards for research and application. Our
extensive benchmark experiments on widely-used datasets demonstrate the
effectiveness of the toolbox. The codebase and trained models have been
publicly available, promoting further research and innovation in the field of
LiDAR segmentation for autonomous driving.

在自动驾驶领域，精确分割 LiDAR 数据对于理解复杂的 3D 环境至关重要。为了解决传统方法中零散、独立的代码库的问题，以及统一推进和公平比较模型的问题，我们引入了 MMDetection3D-lidarseg。作为一种综合性工具箱，它旨在高效地训练和评估最先进的 LiDAR 分割模型。我们支持广泛的分割模型，并集成先进的数据增强技术以增强鲁棒性和泛化性。此外，该工具箱还为多个主流稀疏卷积后端提供支持，以优化计算效率和性能。通过建立统一框架，MMDetection3D-lidarseg 简化了开发和评估，并为研究和应用设定了新的标准。我们在广泛使用的数据集上进行了大量基准实验，证明了该工具箱的有效性。已公开提供代码库和训练模型，以促进自动驾驶 LiDAR 分割领域的进一步研究和创新。