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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.10954 |
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| _version_ | 1866917722344914944 |
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| author | Liu, Hsueh-Ti Derek Agrawala, Maneesh Yuksel, Cem Omernick, Tim Misra, Vinith Corazza, Stefano McGuire, Morgan Zordan, Victor |
| author_facet | Liu, Hsueh-Ti Derek Agrawala, Maneesh Yuksel, Cem Omernick, Tim Misra, Vinith Corazza, Stefano McGuire, Morgan Zordan, Victor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10954 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Unified Differentiable Boolean Operator with Fuzzy Logic Liu, Hsueh-Ti Derek Agrawala, Maneesh Yuksel, Cem Omernick, Tim Misra, Vinith Corazza, Stefano McGuire, Morgan Zordan, Victor Graphics Artificial Intelligence Machine Learning 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. |
| title | A Unified Differentiable Boolean Operator with Fuzzy Logic |
| topic | Graphics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.10954 |