基于RBF的摄像机模型基于射线约束来补偿折射误差
Jaehyun Kim1, Chanyoung Kim1, Seongwook Yoon1
1School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究引入了一种使用高斯过程回归和射线约束的新型摄像机模型,以提高精度与稀疏校准数据的精度. 改进后的模型大大减少了透明屏幕摄像头的射线估计和再投影错误.
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
- 三维重建的3D重建
背景情况:
- 针孔摄像机模型对于3D点估计至关重要.
- 透明屏幕引入复杂的光学扭曲和错误.
- 稀少的校准数据和噪音挑战了准确的摄像机建模.
研究的目的:
- 为具有透明屏蔽的系统开发一个强大的摄像机模型.
- 为了减轻由稀疏的校准数据和观测噪声引起的错误.
- 为了提高3D点估计和射线投影的准确性.
主要方法:
- 利用高斯过程回归 (GPR) 进行强大的噪声处理和密集的残余向量估计.
- 实现了射线约束,以在未观察到的区域执行投影一致性.
- 优化了基于光线基函数 (RBF) 的回归模型,包括GPR和光线约束.
主要成果:
- 在估计的射线误差中平均减少了6%.
- 在再投影错误中平均减少了26%.
- 成功地提高了相机模型的性能,尽管数据稀疏和噪声.
结论:
- 拟议的基于RBF的摄像机模型有效地解决了模拟具有透明屏蔽的摄像机的挑战.
- GPR和射线约束的组合为准确性提供了显著的改善.
- 这种方法为复杂的光学环境中的3D感知提供了更可靠的方法.
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