对于可解释相机校准的知识引导符号回归.
1Department of Mechanical and Production Engineering, Aarhus University, 8200 Aarhus, Denmark.
Journal of imaging
|November 26, 2025
概括
本研究引入了一种用于摄像头校准的新型符号模型发现框架,为传统和神经方法提供了可解释和高效的替代方案. 它准确地识别了镜头扭曲模型,即使数据有限.
科学领域:
- 计算机视觉 计算机视觉
- 光学工程是指光学工程.
- 机器学习 机器学习
背景情况:
- 准确的摄像头校准对于自主导航和机器人等应用至关重要.
- 现有的模型 (针孔,辐射触角) 和神经方法在处理复杂的扭曲,数据要求和计算成本方面存在局限性.
研究的目的:
- 开发一个灵活的,可解释的,资源高效的象征模型发现框架,用于摄像头校准.
- 为了确定镜头特定的投影和扭曲模型,准确地表示光学偏差.
主要方法:
- 利用由物理知识指导的符号回归和遗传编程 (GP).
- 纳入已建立的扭曲模型 (布朗 - 康拉迪,梅 - 里夫斯,卡纳拉 - 布兰特,双球) 作为模块化组件.
- 在符号搜索中嵌入模型,以限制解决方案空间,以便有效的参数拟合和模型选择.
主要成果:
- 该框架成功地确定了各种镜头类型 (鱼眼,全向,天体,传统) 的校准模型.
- 与现有的校准技术取得了可比或优异的结果.
- 证明了对过度装配的稳定性和参数装配的效率.
结论:
- 拟议的符号模型发现框架为摄像头校准提供了一个灵活和可解释的替代方案.
- 它特别适用于稀缺校准数据或有限的计算资源的场景.
- 该方法提供了准确和高效的镜头扭曲建模.
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