在点云细分中用于域调整的组成语义混合
IEEE transactions on pattern analysis and machine intelligence
|August 30, 2023
概括
本研究介绍了构成性语义混合,这是一种用于3D点云语义细分的新型无监督域适应技术. 它通过混合数据样本来增强跨不同传感器和环境的模型概括性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 3D数据处理 3D数据处理
背景情况:
- 对于3D点云语义细分的深度学习模型,由于域转移 (传感器或环境的差异) 导致的概括性困难.
- 现有的域调整方法通常需要特定的数据格式 (例如,范围视图地图) 或多模输入,从而限制了它们的适用性.
- 使用样本混合的图像域适应技术通过操纵输入数据提供了更灵活的方法.
研究的目的:
- 引入第一个无监督域适应技术,用于基于语义和几何样本混合的3D点云语义细分.
- 开发一种方法,以提高深度学习模型的概括能力,以便在不同领域进行点云细分.
- 在无监督和半监督环境中评估拟议方法的有效性.
主要方法:
- 建议在3D点云语义细分中进行无监督域调整的"构成语义混合".
- 引入了一个双分支对称网络架构,同时处理源域和目标域点云.
- 每个网络分支中的两个域的集成数据片段和语义信息 (源标签,目标伪标签).
- 纳入可选的半监督学习,使用有限的人类点级注释.
主要成果:
- 拟议的组合语义混合显著优于无监督域适应点云细分的最先进方法.
- 该方法还在半监督的设置中表现出卓越的性能,进一步提高了对有限注释的准确性.
- 在合成到真实和真实到真实域调整场景中,对LiDAR数据集进行了评估.
结论:
- 组合语义混合是一种有效的无监督域调整技术,用于3D点云语义细分.
- 双分支网络架构成功地利用跨域信息,以改善泛化.
- 该方法提供了一种灵活而强大的解决方案,用于将模型适应各种现实世界的条件.
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