Huu Minh Le and Christopher Zach

We consider the problem of robust fitting with truncated least squares cost function. Existing approaches involve replacing the truncated least squares by a smooth approximation that allows the problem to be solved using variants of Iteratively Re-weighted Least Squares (ILRS). In this work, we propose a new approach based on bi-level optimization that leads to a new algorithm to compute residual weights for the truncated least squares loss, which enables us to incorporate our new approach to existing non-linear least squares solvers. Experimental results show promising results on several large-scale bundle adjustment instances.