A Unified Differentiable Boolean Operator with Fuzzy Logic
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Abstract (arXiv)
This paper presents a unified differentiable boolean operator for implicit solid shape modeling using Constructive Solid Geometry (CSG). Traditional CSG relies on min, max operators to perform boolean operations on implicit shapes. But because these boolean operators are discontinuous and discrete in the choice of operations, this makes optimization over the CSG representation challenging. Drawing inspiration from fuzzy logic, we present a unified boolean operator that outputs a continuous function and is differentiable with respect to operator types. This enables optimization of both the primitives and the boolean operations employed in CSG with continuous optimization techniques, such as gradient descent. We further demonstrate that such a continuous boolean operator allows modeling of both sharp mechanical objects and smooth organic shapes with the same framework. Our proposed boolean operator opens up new possibilities for future research toward fully continuous CSG optimization.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 40 references that are in the map. First posted on arXiv 15 Jul 2024.
Builds on
11 of 40 references.
References · 11
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
- 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 271
- 2019BSP-Net: Generating Compact Meshes via Binary Space Partitioningcited by 206
- 2019CvxNet: Learnable Convex Decompositioncited by 174
- 2017CSGNet: Neural Shape Parser for Constructive Solid Geometrycited by 106
- 2020Analyzing Differentiable Fuzzy Logic Operatorscited by 101
- 2020UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Treecited by 78
- 2021CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assemblycited by 62
- 2021CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsingcited by 51
- 2023Neurosymbolic Models for Computer Graphicscited by 28
Built on by
Citing papers per year: 2024: 1.
1 paper.
- 2024DiffCSG: Differentiable CSG via Rasterizationcited by 7
- 2024DiffCSG: Differentiable CSG via Rasterization2 Sep 2024