Robust Fitting with Truncated Least Squares: A Bilevel Optimization Approach
Paper in proceeding, 2021

We tackle the problem of large-scale robust fitting using the truncated least squares (TLS) loss. Existing approaches commonly optimize this loss by employing a smooth surrogate,which allows the problem to be solved using well-known methods such as Iteratively Re-weighted Least Squares (IRLS). In this work,we present a new approach to optimize the TLS objective,where we propose to reformulate the original problem as a bi-level program. Then,by applying the Optimal Value Reformulation (OVR) technique to this new formulation,we derive a penalty approach to solve for the best fitting models,where the penalty parameters can be adaptively computed. Our final algorithm can be considered as a special instance of IRLS. As a result,we can incorporate our new algorithm into existing IRLS solvers,where we only need to modify the weight evaluation procedure. Our experimental results show promising results on several instances of large-scale bundle adjustment and non-linear refinement for essential matrix fitting.

non linear optimization

robust fitting

truncated least squares

Author

Huu Le

Computer vision and medical image analysis

Christopher Zach

Computer vision and medical image analysis

Proceedings - 2021 International Conference on 3D Vision, 3DV 2021

1392-1400
9781665426886 (ISBN)

9th International Conference on 3D Vision, 3DV 2021
Virtual, Online, United Kingdom,

Subject Categories

Computational Mathematics

Control Engineering

Signal Processing

DOI

10.1109/3DV53792.2021.00146

More information

Latest update

3/8/2022 7