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A comment on Show HN: 3D Meshes of Signed Distance Functions in Python. Parent comment

Re:Show HN: 3D Meshes of Signed Distance Functions in Python by sjkelly on Tuesday February 16, 2021 @10:12PM (#26162192)
Generative design is almost always in SDF form. Things like point clouds, images, and 3DNN also dovetail nicely. SIMP in topology optimization is a good example also. I believe alot of SDF applications are still held back by mesh extraction. There is no silver bullet that can handle adaptive methods, sharp features, and generate a manifold.

SDF and mesh extraction are one of my favorite areas of research. I think it is very important for additive manufacturing in particular. The value will be hybrid SDF and spline methods for complex and highly integrated applications such as fluid and heat transfer or compliant mechanisms.

Modeling a box or cylinder with SDF isn't the right application IMO. Optimized topology for a given PDE is.

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Re:Show HN: 3D Meshes of Signed Distance Functions in Python by tdotuos on Thursday February 25, 2021 @12:08AM (#26259918)
> Modeling a box or cylinder with SDF isn't the right application IMO. Optimized topology for a given PDE is.

Like anything in engineering and design, IMO, it depends on who you are what you are doing.

> SDF and mesh extraction are one of my favorite areas of research.

Why convert a SDF to a mesh to do visualization and engineering analysis? Even with FDM, which can be thought of a 2D filling problem like scan conversion once you have a SDF, meshless may be the way forward with additive manufacturing as well. One can think of reasons you would have to do mesh extraction, but for niche areas of manufacturing.

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