An Experimental Study of SOTA LiDAR Segmentation Models
This work addresses the lack of practical comparisons for LiDAR segmentation models, aiding engineers in model selection and inspiring more practical designs, though it is incremental as it focuses on benchmarking existing methods.
The paper conducted a comprehensive experimental comparison of state-of-the-art LiDAR segmentation models (point-, voxel-, and range image-based) by evaluating metrics like inference latency, mIoU scores, and GPU memory usage, providing insights for engineers and researchers.
Point cloud segmentation (PCS) is to classify each point in point clouds. The task enables robots to parse their 3D surroundings and run autonomously. According to different point cloud representations, existing PCS models can be roughly divided into point-, voxel-, and range image-based models. However, no work has been found to report comprehensive comparisons among the state-of-the-art point-, voxel-, and range image-based models from an application perspective, bringing difficulty in utilizing these models for real-world scenarios. In this paper, we provide thorough comparisons among the models by considering the LiDAR data motion compensation and the metrics of model parameters, max GPU memory allocated during testing, inference latency, frames per second, intersection-over-union (IoU) and mean IoU (mIoU) scores. The experimental results benefit engineers when choosing a reasonable PCS model for an application and inspire researchers in the PCS field to design more practical models for a real-world scenario.