COLA: COarse-LAbel Multisource LiDAR Semantic Segmentation for Autonomous Driving

IF 9.4 1区 计算机科学 Q1 ROBOTICS
Jules Sanchez;Jean-Emmanuel Deschaud;François Goulette
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引用次数: 0

Abstract

LiDAR semantic segmentation (LSS) for autonomous driving has been a growing field of interest in recent years. Datasets and methods have appeared and expanded very quickly, but methods have not been updated to exploit this new data availability and rely on the same classical datasets. Different ways of performing LSS training and inference can be divided into several subfields, which include the following: domain generalization, source-to-source segmentation, and pretraining. In this work, we aim to improve results in all of these subfields with the novel approach of multisource training. Multisource training relies on the availability of various datasets at training time. To overcome the common obstacles in multisource training, we introduce the coarse labels and call the newly created multisource dataset COLA. We propose three applications of this new dataset that display systematic improvement over single-source strategies: COLA-DG for domain generalization (+10% ), COLA-S2S for source-to-source segmentation (+5.3% ), and COLA-PT for pretraining (+12% ). We demonstrate that multisource approaches bring systematic improvement over single-source approaches.
面向自动驾驶的粗标签多源激光雷达语义分割
近年来,用于自动驾驶的激光雷达语义分割(LSS)已成为一个越来越受关注的领域。数据集和方法已经出现并迅速扩展,但方法尚未更新以利用这种新的数据可用性,并依赖于相同的经典数据集。执行LSS训练和推理的不同方法可以分为几个子领域,其中包括:域泛化、源到源分割和预训练。在这项工作中,我们的目标是通过多源训练的新方法来改善所有这些子领域的结果。多源训练依赖于训练时各种数据集的可用性。为了克服多源训练中常见的障碍,我们引入了粗糙标签,并将新创建的多源数据集称为COLA。我们提出了这个新数据集的三种应用,它们比单源策略显示出系统的改进:COLA-DG用于域泛化(+10%),COLA-S2S用于源到源分割(+5.3%),COLA-PT用于预训练(+12%)。我们证明了多源方法比单源方法带来了系统的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Robotics
IEEE Transactions on Robotics 工程技术-机器人学
CiteScore
14.90
自引率
5.10%
发文量
259
审稿时长
6.0 months
期刊介绍: The IEEE Transactions on Robotics (T-RO) is dedicated to publishing fundamental papers covering all facets of robotics, drawing on interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, and beyond. From industrial applications to service and personal assistants, surgical operations to space, underwater, and remote exploration, robots and intelligent machines play pivotal roles across various domains, including entertainment, safety, search and rescue, military applications, agriculture, and intelligent vehicles. Special emphasis is placed on intelligent machines and systems designed for unstructured environments, where a significant portion of the environment remains unknown and beyond direct sensing or control.
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