Domain Mapping for Volumetric Parameterization using Harmonic Functions

Abstract

Volumetric parameterization problem refers to parameterization of both the interior and boundary of a 3D model. It is a much harder problem compared to surface parameterization where a parametric representation is worked out only for the boundary of a 3D model (which is a surface). Volumetric parameterization is typically helpful in solving complicated geometric problems pertaining to shape matching, morphing, path planning of robots, and isogeometric analysis etc. A novel method is proposed in which a volume parameterization is developed by mapping a general nonconvex (genus-0) domain to its topologically equivalent convex domain. In order to achieve a continuous and bijective mapping of a domain, first we use the harmonic function to establish a potential field over the domain. The gradients of the potential values are used to track the streamlines which originate from the boundary and converge to a single point, referred to as the shape center. Each streamline approaches the shape center at a unique polar angle () and an azimuthal angle () . Once all the three parameters (potential value , polar angle , azimuthal angle ) necessary to represent any point in the given domain are available, the domain is said to be parameterized. Using our method, given a 3D non-convex domain, we can parameterize the surface as well as the interior of the domain. The proposed method is implemented and the algorithm is tested on many standard cases to demonstrate the effectiveness.

Measure controllable volumetric mesh parameterization

“…They also proposed an algorithm to decompose a volume into the direct product of a two-dimensional (2D) surface and a one-dimensional (1D) curve and then traced the integral curve along the harmonic function in [10]. In 2014, by combining harmonic map and streamline approach, Gupta et al [11] presented an approach for the problem of volumetric parameterization of a general nonconvex (genus-0) domain to its topologically equivalent convex domain.…”

A Volumetric Conformal Mapping Approach for Clustering White Matter Fibers in the Brain

“…Shape parameterization is a well researched area in the computational geometry community [1,2]. In computational anatomy, many algorithms have been devoted to surface parameterization [3–6] and its applications to cortical surface matching and registration [7].…”