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.

翻译：点云分割（PCS）旨在对点云中的每个点进行分类。该任务使机器人能够解析其三维环境并实现自主运行。根据点云表示方式的不同，现有PCS模型可大致分为基于点、基于体素和基于距离图像的模型。然而，目前尚未有研究从应用视角对基于点、体素和距离图像的先进模型进行全面比较，这为在实际场景中应用这些模型带来了困难。本文通过综合考虑LiDAR数据运动补偿、模型参数量、测试期间最大GPU内存占用、推理延迟、每秒帧数、交并比（IoU）及平均交并比（mIoU）等指标，对这些模型进行了全面对比。实验结果有助于工程师为具体应用选择合理的PCS模型，并启发PCS领域的研究者设计更适用于实际场景的模型。