Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/3DV53792.2021.00146 | 2021 INTERNATIONAL CONFERENCE ON 3D VISION (3DV 2021) |
DocType | ISSN | Citations |
Conference | 2378-3826 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huu Le | 1 | 22 | 6.45 |
| Christopher Zach | 2 | 1457 | 84.01 |