A Survey of Methods for Converting Unstructured Data to CSG Models

TL;DR

This survey reviews various methods for converting unstructured 3D data into constructive solid geometry (CSG) models, covering techniques from solid modeling, program synthesis, evolutionary algorithms, and deep learning.

Contribution

It provides a comprehensive overview of existing approaches for CSG conversion from unstructured data, highlighting recent advances and diverse methodologies.

Findings

Deep learning methods show promising results in CSG conversion.

Evolutionary algorithms effectively optimize CSG representations.

Program synthesis techniques enable automated generation of solid models.

Abstract

The goal of this document is to survey existing methods for recovering CSG representations from unstructured data such as 3D point-clouds or polygon meshes. We review and discuss related topics such as the segmentation and fitting of the input data. We cover techniques from solid modeling and CAD for polyhedron to CSG and B-rep to CSG conversion. We look at approaches coming from program synthesis, evolutionary techniques (such as genetic programming or genetic algorithm), and deep learning methods. Finally, we conclude with a discussion of techniques for the generation of computer programs representing solids (not just CSG models) and higher-level representations (such as, for example, the ones based on sketch and extrusion or feature based operations).