三焦相对姿势从线到点的三焦相对姿势
IEEE transactions on pattern analysis and machine intelligence
|October 11, 2023
概括
本研究引入了一种使用三视图和点线对应的摄像头姿势估计的新方法. 这种新的方法有效地解决了现有方法失败的复杂问题,提高了3D重建的准确性.
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
- 几何深度学习 几何深度学习
背景情况:
- 相对相机姿势估计对于3D重建和场景理解至关重要.
- 现有的方法与涉及多个视图中的点线对应的最小问题作斗争.
- 对于这些复杂的几何约束,Grobner基础方法是计算密集的.
研究的目的:
- 开发一种有效的解决方案,用于三视相对相机姿势估计中的两个最小问题.
- 解决涉及点和线对应的情况,这些对当前最先进的技术来说是难以处理的.
- 为了提高结构从运动 (SfM) 初始化的稳定性和适用性.
主要方法:
- 引入了一个名为MINUS的新型同位素延续 (HC) 解决方案框架.
- 专门的HC方法对三视图最小问题的通用案例 (三点/一行,三点/两行).
- 描述了这些最小问题的解决方案的数量.
主要成果:
- MINUS框架显著加速了用于摄像头姿势估计的HC解决.
- 模拟实验证明了图像噪声下的数值稳定性和稳定性.
- 现实世界的实验证实了SIFT特征在三视图重建中的点与线对应的有效性.
- 成功解决了具有稀疏或噪音匹配的具有挑战性的案例,在传统的SfM初始化失败的情况下.
结论:
- 拟议的HC解决方案提供了一个高效和强大的解决方案,用于挑战三视角相对相机立场估计问题.
- 这种方法扩大了SfM的功能,特别是在特征数据有限或杂的场景中.
- 这些发现有助于从多个角度进行更可靠,更准确的3D场景重建.
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