Robust Fitting with Truncated Least Squares: A Bilevel Optimization Approach


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.

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  Important Dates

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Paper registration July 23 30, 2021
Paper submission July 30, 2021
Supplementary August 8, 2021
Tutorial submission August 15, 2021
Tutorial notification August 31, 2021
Rebuttal period September 16-22, 2021
Paper notification October 1, 2021
Camera ready October 15, 2021
Demo submission July 30 Nov 15, 2021
Demo notification Oct 1 Nov 19, 2021
Tutorial November 30, 2021
Main conference December 1-3, 2021