When dealing with datasets comprising high‐dimensional points, it is usually advantageous
to discover some data structure. A fundamental information needed to this aim is the …

The objective of segmentation on point clouds is to spatially group points with similar
properties into homogeneous regions. Segmentation is a fundamental issue in processing …

In this work, we provide a mathematical proof that diffusion models encode data manifolds
by approximating their normal bundles. Based on this observation we propose a novel …

Recently, manifold learning has been widely exploited in pattern recognition, data analysis,
and machine learning. This paper presents a novel framework, called Riemannian manifold …

In this work, we propose a novel framework for estimating the dimension of the data manifold
using a trained diffusion model. A diffusion model approximates the score function ie the …