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Iterated Lifting for Robust Cost Optimization

Abstract : Optimization of latent model parameters using robust formulations usually creates a large number of local minima due to the quasi-convex shape of the underlying robust kernel. Lifting the robust kernel, i.e. embedding the problem into a higher-dimensional space, leads to significantly better local minima in a range of 3D computer vision problems (e.g. [10, 11, 12]). In this work we propose to iterate this lifting construction to obtain a more gradual lifting scheme for a given target kernel. Thus, a robust kernel is not directly lifted against the (non-robust) quadratic kernel, but initially against a different , less robust kernel. This process is iterated until the quadratic kernel is reached to allow utilization of efficient non-linear least-squares solvers. We demonstrate in synthetic and real problem instances that iterated lifting generally reaches better local minima than IRLS and standard half-quadratic lifting.
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Submitted on : Tuesday, February 27, 2018 - 8:58:21 AM
Last modification on : Thursday, March 1, 2018 - 1:03:36 AM
Long-term archiving on: : Monday, May 28, 2018 - 11:45:07 AM


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  • HAL Id : hal-01718012, version 1


Christopher Zach, Guillaume Bourmaud. Iterated Lifting for Robust Cost Optimization. BMVC , 2017, London, United Kingdom. ⟨hal-01718012⟩



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