玻璃墙壁的单眼深度估计与背景:一个新的数据集和方法
IEEE transactions on pattern analysis and machine intelligence
|August 25, 2023
概括
本研究引入了一种单眼深度估计的新方法,解决透明玻璃墙所带来的挑战. 它利用结构和反射背景来准确预测自主导航中的深度.
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
- 人工智能的人工智能
背景情况:
- 传统的单眼深度估计在玻璃墙等透明表面上失败.
- 标注玻璃墙深度是很困难的,因为传感器和以前的方法无法处理透明度.
研究的目的:
- 开发第一个数据集 (GW-Depth) 和使用透明玻璃墙进行单眼深度估计的方法.
- 为了在具有具有挑战性的透明表面的环境中实现准确的深度感知.
主要方法:
- 引入了GW-Depth数据集,其中有注释的玻璃墙深度,细分面具和边缘线段.
- 提出了一种新的单眼深度估计方法,利用玻璃结构上下文 (边界线) 和反射上下文 (深度差异).
主要成果:
- 拟议的双语境方法显著提高了玻璃墙景象的深度估计准确度.
- 实验结果表明,与现有的最先进的方法相比,性能优越.
结论:
- 在玻璃墙的存在下,为单眼深度估计提供了第一个可行的解决方案.
- 该方法的适应性和准确性使其适用于自主导航等应用.
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