对比主流激光雷达分割模型，为实际应用选型提供依据。
An Experimental Study of SOTA LiDAR Segmentation Models
- 从点、体素、范围图像三类方法全面比较性能
- 涵盖精度、速度、内存占用等7项关键指标
- 适合机器人导航与自动驾驶系统开发人员参考
点云分割（PCS）旨在对点云中的每个点进行分类，使机器人能够解析三维环境并实现自主运行。根据点云表示方式，现有PCS模型大致可分为基于点、体素和范围图像的模型。然而，目前尚无研究从应用角度对最先进点、体素和范围图像类模型进行系统性对比，给实际场景中模型选择带来困难。本文通过考虑激光雷达数据运动补偿，并综合评估模型参数量、测试时最大GPU显存占用、推理延迟、帧率、交并比（IoU）及均值交并比（mIoU）等指标，对各类模型进行了详尽比较。实验结果有助于工程师在实际应用中合理选择PCS模型，也为该领域研究人员设计更贴近真实场景的实用模型提供启发。
原文摘要 · Abstract (English)
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.
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