无监督的单眼深度和摄像头姿势估计使用多个面具和几何一致性约束
Xudong Zhang1, Baigan Zhao2, Jiannan Yao2
1School of Information Science and Technology, Nantong University, Nantong 226019, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种新的无监督学习方法,用于从视频中估计深度和摄像头姿势. 它使用面具技术和几何一致性在具有挑战性的场景中提高了准确性,优于现有的无监督方法.
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:
- 从视频中估计场景深度和摄像头姿势对于3D重建,视觉导航和增强现实至关重要.
- 现有的无监督学习方法在具有挑战性的视觉场景中与动态对象和遮作斗争.
研究的目的:
- 开发一种新的无监督学习框架,用于强大的深度和摄像头姿势估计.
- 解决当前处理动态物体和封闭区域的方法的局限性.
主要方法:
- 实施多重掩盖技术,以识别和排除损失计算中的异常值.
- 利用已识别的异常值作为监督信号来训练面具估计网络.
- 引入了几何一致性约束,以减轻照明变化和增强姿势估计.
主要成果:
- 拟议的框架有效地减轻了具有挑战性的场景对深度和姿势估计的负面影响.
- 基蒂数据集的实验结果显示,与其他无监督方法相比,其性能优越.
- 面具估计网络和几何约束作为有效的监督信号.
结论:
- 新的无监督框架显著提高了深度和摄像头姿势估计的准确性和稳定性.
- 面具技术和几何一致性的整合为视觉SLAM和相关领域的未来研究提供了有希望的方向.
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