用简单采样法替代复杂畸变求解器，效果更好且更快。
Are Minimal Radial Distortion Solvers Necessary for Relative Pose Estimation?
- 用采样畸变参数+高效针孔求解器，代替复杂畸变求解
- 实验表明该方法在多个数据集上精度更高、速度更快
- 适合需要快速准确位姿估计的工程应用
相对位姿估计是结构光重建等应用的基础。通常在RANSAC循环中使用最小化求解器。针孔相机的高效求解器已成熟，但几乎所有相机都存在径向畸变。忽略畸变会导致显著性能下降。然而，考虑畸变的最小求解器实现复杂、运行慢。本文对比了传统畸变求解器与一种简单方法：在高效针孔求解器中引入采样畸变参数。大量实验在多个数据集和RANSAC变体上验证，该方法在运行速度更快的同时，表现相似或优于最精确的最小畸变求解器，且远超快速非最小求解器。结果明确表明，实践中并不需要复杂的径向畸变求解器。代码与基准测试见https://github.com/kocurvik/rd。
原文摘要 · Abstract (English)
Estimating the relative pose between two cameras is a fundamental step in many applications such as Structure-from-Motion. The common approach to relative pose estimation is to apply a minimal solver inside a RANSAC loop. Highly efficient solvers exist for pinhole cameras. Yet, (nearly) all cameras exhibit radial distortion. Not modeling radial distortion leads to (significantly) worse results. However, minimal radial distortion solvers are significantly more complex than pinhole solvers, both in terms of run-time and implementation efforts. This paper compares radial distortion solvers with a simple-to-implement approach that combines an efficient pinhole solver with sampled radial distortion parameters. Extensive experiments on multiple datasets and RANSAC variants show that this simple approach performs similarly or better than the most accurate minimal distortion solvers at faster run-times while being significantly more accurate than faster non-minimal solvers. We clearly show that complex radial distortion solvers are not necessary in practice. Code and benchmark are available at https://github.com/kocurvik/rd.
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