An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models
This work addresses the problem of fragmented development and benchmarking for LiDAR segmentation models in autonomous driving, though it is incremental as it builds on existing tools and methods.
The authors tackled the lack of unified frameworks for LiDAR segmentation in autonomous driving by introducing MMDetection3D-lidarseg, a toolbox that streamlines training and evaluation, as demonstrated through benchmark experiments on widely-used datasets.
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.