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为了在LiDAR语义细分中实现单源域概括的增强的表示学习,在LiDAR语义细分中实现单源域概括.

Hyeonseong Kim, Yoonsu Kang, Changgyoon Oh

    IEEE transactions on pattern analysis and machine intelligence
    |January 15, 2026
    PubMed
    概括

    本研究介绍了DGLSS++,这是LiDAR语义细分中的域概括的新方法. 它在不同的LiDAR传感器配置和场景分布上确保了强大的性能,优于现有的方法.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 机器人技术 机器人技术 机器人技术

    背景情况:

    • 3D深度学习模型在LiDAR语义细分方面表现出色,但在看不见的环境中难以处理域移动.
    • 域泛化对于强大的自动驾驶感知系统至关重要.
    • 现有的方法往往无法有效地将单个源域推广到不同的现实场景.

    研究的目的:

    • 提出DGLSS++,一种用于LiDAR语义细分中的域概括的表示学习方法.
    • 在源域和未见域中确保强大的性能,即使仅在源域上进行训练.
    • 为解决由 LiDAR 传感器配置和场景分布的变化引起的域移位问题.

    主要方法:

    • 开发了DGLSS++用于LiDAR语义细分中的单源域概括.
    • 通过增强模拟了看不见的域 (零散到密集和密集到零散).
    • 引入了通用化掩盖稀疏性不变特征一致性 (GMSIFC) 和局部化语义相关性一致性 (LSCC),用于可通用的表示学习.
    • GMSIFC通过一种新的掩盖策略将跨域的稀疏特征对齐;LSCC保持本地语义相关性.

    主要成果:

    • 与无监督域调整 (UDA) 和域泛化 (DG) 基线相比,DGLSS++表现优越.

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  • 该方法在各种LiDAR传感器配置和场景分布中实现了强大的性能.
  • 实验使用了四个现实世界数据集,验证了拟议方法的有效性.

    • 结论:

    • DGLSS++有效地解决了自动驾驶的LiDAR语义细分领域的差距.
    • 拟议的约束 (GMSIFC和LSCC) 便于从单个源域进行可靠的概括.
    • 开发的方法为现实世界自动驾驶感知系统提供了一个有前途的解决方案.