Consensus maximization is widely used in robust model fitting. Here, we achieve globally optimal consensus maximization by Branch-and-Bound framework and draw the idea of interval arithmetic-based bound calculation back on the map. We provide the detailed derivation of interval arithmetic-based bound calculation for consensus maximization problems with both linear and quasi-convex residuals. Extensive experiments show that the proposed method can better deal with larger number of data points and higher outlier ratios than existing global methods.
Linear:
For a linear model, two kinds of constraints are employed to fix the unknown scale of model. (Linear regression and Unit Norm constriant) Hence, you can choose "GoIA-LR" and "GoIA-UN" to slove the linear problem.
- Synthetic linear model.
- Plane fitting.
- Translation estimation.
- Decomposed homography estimation.
Nonlinear:
- Affine registration.
- Triangulation.
- Clone this repository.
- Run function "demo()" in MATLAB.
Email: yiruwang18@fudan.edu.cn