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Caner Korkmaz

2 accepted papers

2026

Parallelised Differentiable Straightest Geodesics for 3D Meshes

CVPR 2026

Machine learning has been progressively generalised to operate within non-Euclidean domains, but geometrically accurate methods for learning on surfaces are still falling behind. The lack of closed-form Riemannian operators, the non-differentiability of their discrete counterparts, and poor parallel

Cited by 0SourceScholar
2025

CuMPerLay: Learning Cubical Multiparameter Persistence Vectorizations

ICCV 2025poster

We present CuMPerLay, a novel differentiable vectorization layer that enables the integration of Cubical Multiparameter Persistence (CMP) into deep learning pipelines. While CMP presents a natural and powerful way to topologically work with images, its use is hindered by the complexity of multifiltr…