Square Root Normal Fields for Lipschitz surfaces and the Wasserstein Fisher Rao metric

TL;DR

This paper extends the SRNF framework to Lipschitz surfaces, showing its distance measure aligns with the Wasserstein Fisher Rao metric, and explores implications for shape analysis and measure space optimization.

Contribution

It generalizes the SRNF-WFR equivalence from piecewise linear to Lipschitz surfaces and characterizes the SRNF transform's properties for spherical surfaces.

Findings

SRNF distance on Lipschitz surfaces equals WFR distance between measures

Characterization of SRNF transform's non-injectivity for spherical surfaces

WFR metric as an optimization over diffeomorphisms

Abstract

The Square Root Normal Field (SRNF) framework is a method in the area of shape analysis that defines a (pseudo) distance between unparametrized surfaces. For piecewise linear (PL) surfaces it was recently proved that the SRNF distance between unparametrized surfaces is equivalent to the Wasserstein Fisher Rao (WFR) metric on the space of finitely supported measures on $S^2$. In the present article we extend this point of view to a much larger set of surfaces; we show that the SRNF distance on the space of Lipschitz surfaces is equivalent to the WFR distance between Borel measures on $S^2$. For the space of spherical surfaces this result directly allows us to characterize the non-injectivity and the (closure of the) image of the SRNF transform. In the last part of the paper we further generalize this result by showing that the WFR metric for general measure spaces can be interpreted as an optimization problem over the diffeomorphism group of an independent background space.