We propose a novel mixed-integer programming (MIP) formulation for generating precise
sparse correspondences for highly non-rigid shapes. To this end, we introduce a projected …

We present the first spatial-spectral joint consistency network for self-supervised dense
correspondence mapping between non-isometric shapes. The task of alignment in non …

Abstract 3D scene flow estimation is a vital tool in perceiving our environment given depth or
range sensors. Unlike optical flow, the data is usually sparse and in most cases partially …

The problem of manufacturing process classification is to identify manufacturing processes
that are suitable for a given part design. Previous research on automating manufacturing …

A proof of the optimality of the eigenfunctions of the Laplace--Beltrami operator (LBO) in
representing smooth functions on surfaces is provided and adapted to the field of applied …

Multidimensional scaling (MDS) is a family of methods that embed a given set of points into
a simple, usually flat, domain. The points are assumed to be sampled from some metric …

Non-isometric shape correspondence remains a fundamental challenge in computer vision.
Traditional methods using Laplace-Beltrami operator (LBO) eigenmodes face limitations in …

Geometric operators are common objects in surface-based shape analysis and geometry
processing. While the intrinsic Laplace–Beltrami operator has been a ubiquitous choice …

We propose a novel spectral-teacher spatial-student (STS) learning paradigm for non-rigid
dense shape correspondence. Current methods can be segmented into two categories; …

Shape recognition deals with the study geometric structures. Modern surface processing
methods can cope with non-rigidity—by measuring the lack of isometry, deal with similarity …