Shape‐Up: Shaping Discrete Geometry with Projections

TLDR

Shape constraints preserve or prescribe the geometry of subsets of points in a dataset, such as polygons, one‑ring cells, volume elements, or feature curves. The authors propose a unified optimization framework for geometry processing that relies on these shape constraints. Their method introduces a shape proximity function that measures the distance to a least‑squares fitted target shape and uses shape projection operators to relocate vertices minimally, thereby minimizing the proximity. The resulting algorithm is simple, robust, and efficient, enabling planar and circular mesh generation, shape‑space exploration, mesh quality improvement, shape‑preserving deformation, and conformal parametrization, and it achieves results comparable to or better than state‑of‑the‑art methods.

Abstract

Abstract We introduce a unified optimization framework for geometry processing based on shape constraints. These constraints preserve or prescribe the shape of subsets of the points of a geometric data set, such as polygons, one‐ring cells, volume elements, or feature curves. Our method is based on two key concepts: a shape proximity function and shape projection operators. The proximity function encodes the distance of a desired least‐squares fitted elementary target shape to the corresponding vertices of the 3D model. Projection operators are employed to minimize the proximity function by relocating vertices in a minimal way to match the imposed shape constraints. We demonstrate that this approach leads to a simple, robust, and efficient algorithm that allows implementing a variety of geometry processing applications, simply by combining suitable projection operators. We show examples for computing planar and circular meshes, shape space exploration, mesh quality improvement, shape‐preserving deformation, and conformal parametrization. Our optimization framework provides a systematic way of building new solvers for geometry processing and produces similar or better results than state‐of‐the‐art methods.