积极差异采样用于与附属网络的立体声匹配
概括
这项研究引入了主动差异采样,以优化立体视觉的深度测量. 通过学习像素智能的差异质量,它可以提高密度差异估计,特别是在具有挑战性的领域.
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:
- 稀疏的深度测量用于指导立体视觉中的密度差异估计.
- 现有的方法假定随机抽样,导致在具有挑战性和良好估计的地区使用数据效率低下.
研究的目的:
- 为优化深度测量实用程序引入一个主动差异采样 (ADS) 问题.
- 通过任意抽样预算来提高密度差异估计的性能.
主要方法:
- 学习辅助网络来预测深度立体声模型的像素智能差异质量.
- 为分层质量地图学习实施硬软先前监督.
- 建议以差异质量为指导的贝叶斯优化抽样政策.
主要成果:
- 在各种立体声架构和数据集中证明了有效性.
- 在不同的采样速率下,超越现有的固定和自适应采样方法.
- 当将其泛化到异质的未见域时,显示了实质性的改进.
结论:
- 主动差异采样增强了稀疏深度测量的实用性,用于密集差异估计.
- 拟议的方法提供了一种灵活和有效的方法,用于在立体视觉中采样策略.
- 这种方法可以很好地对多样化和以前未见过的数据领域进行概括.
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