The strength of a simplex is the key to a continuous isometry classification of Euclidean clouds of unlabelled points

03/23/2023
by   Vitaliy Kurlin, et al.
0

This paper solves the continuous classification problem for finite clouds of unlabelled points under Euclidean isometry. The Lipschitz continuity of required invariants in a suitable metric under perturbations of points is motivated by the inevitable noise in measurements of real objects. The best solved case of this isometry classification is known as the SSS theorem in school geometry saying that any triangle up to congruence (isometry in the plane) has a continuous complete invariant of three side lengths. However, there is no easy extension of the SSS theorem even to four points in the plane partially due to a 4-parameter family of 4-point clouds that have the same six pairwise distances. The computational time of most past metrics that are invariant under isometry was exponential in the size of the input. The final obstacle was the discontinuity of previous invariants at singular configurations, for example, when a triangle degenerates to a straight line. All the challenges above are now resolved by the Simplexwise Centred Distributions that combine inter-point distances of a given cloud with the new strength of a simplex that finally guarantees the Lipschitz continuity. The computational times of new invariants and metrics are polynomial in the number of points for a fixed Euclidean dimension.

READ FULL TEXT
research
07/18/2022

Computable complete invariants for finite clouds of unlabeled points under Euclidean isometry

A finite cloud of unlabeled points is the simplest representation of man...
research
05/09/2022

Exactly computable and continuous metrics on isometry classes of finite and 1-periodic sequences

The inevitable noise in real measurements motivates the problem to conti...
research
07/22/2020

The mergegram of a dendrogram and its stability

This paper extends the key concept of persistence within Topological Dat...
research
11/08/2021

Isometry invariant shape recognition of projectively perturbed point clouds by the mergegram extending 0D persistence

Rigid shapes should be naturally compared up to rigid motion or isometry...
research
05/05/2022

Low Dimensional Invariant Embeddings for Universal Geometric Learning

This paper studies separating invariants: mappings on d-dimensional semi...
research
02/19/2019

Shapes from Echoes: Uniqueness from Point-to-Plane Distance Matrices

We study the problem of localizing a configuration of points and planes ...

Please sign up or login with your details

Forgot password? Click here to reset