# A Convex Optimization Framework for Regularized Geodesic Distances

arXiv: https://arxiv.org/abs/2305.13101 · HTML: https://mlsanity.com/paper/2305.13101/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2023.

## Built on by (most cited first, 1 of 1)

- [Iskra: A System for Inverse Geometry Processing](https://mlsanity.com/paper/2602.12105/) (2026)

## Abstract (arXiv)

We propose a general convex optimization problem for computing regularized geodesic distances. We show that under mild conditions on the regularizer the problem is well posed. We propose three different regularizers and provide analytical solutions in special cases, as well as corresponding efficient optimization algorithms. Additionally, we show how to generalize the approach to the all pairs case by formulating the problem on the product manifold, which leads to symmetric distances. Our regularized distances compare favorably to existing methods, in terms of robustness and ease of calibration.