Detection and Characterization of Intrinsic Symmetry

TL;DR

This paper introduces a comprehensive framework for detecting and characterizing intrinsic symmetries in 3D shapes, combining correspondence voting and functional map techniques to handle overlapping symmetric regions and complex transformations.

Contribution

The novel framework integrates correspondence space voting with transformation space mapping, including a new cost matrix, to improve symmetry detection and characterization in complex 3D shapes.

Findings

Successfully detects overlapping intrinsic symmetries in complex 3D shapes.

Employs a novel cost matrix to measure symmetry transformation complexity.

Creates a semi-metric symmetry space representing transformation complexities.

Abstract

A comprehensive framework for detection and characterization of overlapping intrinsic symmetry over 3D shapes is proposed. To identify prominent symmetric regions which overlap in space and vary in form, the proposed framework is decoupled into a Correspondence Space Voting procedure followed by a Transformation Space Mapping procedure. In the correspondence space voting procedure, significant symmetries are first detected by identifying surface point pairs on the input shape that exhibit local similarity in terms of their intrinsic geometry while simultaneously maintaining an intrinsic distance structure at a global level. Since different point pairs can share a common point, the detected symmetric shape regions can potentially overlap. To this end, a global intrinsic distance-based voting technique is employed to ensure the inclusion of only those point pairs that exhibit significant symmetry. In the transformation space mapping procedure, the Functional Map framework is employed to generate the final map of symmetries between point pairs. The transformation space mapping procedure ensures the retrieval of the underlying dense correspondence map throughout the 3D shape that follows a particular symmetry. Additionally, the formulation of a novel cost matrix enables the inner product to succesfully indicate the complexity of the underlying symmetry transformation. The proposed transformation space mapping procedure is shown to result in the formulation of a semi-metric symmetry space where each point in the space represents a specific symmetry transformation and the distance between points represents the complexity between the corresponding transformations. Experimental results show that the proposed framework can successfully process complex 3D shapes that possess rich symmetries.