mlsanityData to 7 Oct 2026

A Survey of Methods for Converting Unstructured Data to CSG Models

arXiv 2305.01220 ↗First posted 2 May 2023cs.GR cs.CVcited by 3

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Abstract (arXiv)

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).

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 54 of its 141 references that are in the map. First posted on arXiv 2 May 2023.

Builds on

54 of 141 references.

References · 54

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38 more
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50 most cited of 54 All 54 (JSON)

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2024Split-and-Fit: Learning B-Reps via Structure-Aware Voronoi Partitioningcited by 17
  2. 2025CAGE: Continuity-Aware edGE Network Unlocks Robust Floorplan Reconstructioncited by 3
  3. 2023P2CADNet: An End-to-End Reconstruction Network for Parametric 3D CAD Model from Point Cloudscited by 1