终身单一深度:为多域单眼度量深度估计而进行终身学习
IEEE transactions on neural networks and learning systems
|October 24, 2023
概括
本研究引入了一种新的终身学习 (LL) 方法,用于自主系统中准确的度量深度估计. 该方法有效地处理域变化和尺度差异,提高深度地图质量.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
背景情况:
- 终身学习 (LL) 模型对于自动驾驶和机器人导航至关重要,需要衡量深度估计.
- 现有的LL方法在与传感器依赖的深度地图变化和大域差距作斗争,限制它们在相对深度估计上.
- 挑战包括深度尺度变化,显著的域差距,以及需要自动化域意识推理.
研究的目的:
- 开发一个终身学习模型,用于准确的度量深度估计.
- 为了应对自主系统中深度尺度变化,域间隙和实时推理的挑战.
- 提高终身深度学习模型的效率,稳定性和可塑性.
主要方法:
- 一个轻量级的多头框架,用于规模意识的深度学习,以解决深度规模的不平衡.
- 一个意识到不确定性的终身学习解决方案,以处理重大领域差距.
- 一种在线域特定预测器选择方法,用于实时,自动化域意识推理.
主要成果:
- 提出的方法证明了终身深度学习的效率,稳定性和可塑性.
- 在度量深度估计方面实现了8%-15%的基准改进.
- 成功地解决了深度尺度变化和大域差距的问题.
结论:
- 开发的框架促进了终身度量深度学习,克服了现有方法的局限性.
- 该方法为自主应用程序中的实时,域意识深度推理提供了实用解决方案.
- 该方法提高了深度估计模型在动态环境中的性能和适应性.
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