oce VS solvespace

Have a look at https://github.com/tpaviot/oce/ Not sure if there is a go wrapper for it. There is a python library wrapping opencascade already.

Some parts of the 3MF are actually good. I like the spec documents winding order of the meshes. The format includes units, and these optional 4x4 transformation matrices — both are useful.

As for need of the new formats, for one, modern CAD formats are insanely complex. These IGES/STEP/BREP files require many megabytes of very complicated code to deal with, such as this library https://github.com/tpaviot/oce These formats may even contain proprietary extensions. Also, they need non-trivial processing power to handle. Many people wouldn’t want a Core i7 with gigabytes of RAM in their 3D printers, inflates hardware cost and software complexity.

Besides, we now have high-resolution 3D scanners, and CAE software which algorithmically optimizes models by running numerical simulations. They both output triangle meshes instead of CAD files. Scanners often output point clouds one can convert into triangles, but hard to convert into CAD formats.

I just don’t like the 3MF implementation too much. XML is fine for kilobytes of data, but not many megabytes. If I would be designing that format, I would probably made it binary. Maybe EBML https://en.wikipedia.org/wiki/Extensible_Binary_Meta_Languag... would work for that; it does fine for MKV videos, which is also a huge pile of structured data with non-trivial performance constraints for producers and especially parsers.

Another minor thing, it was not the best idea to make name start with a digit. Most programming languages forbid identifiers like that for their classes / functions / namespaces / modules.

Cadquery uses opencascade under the hood. I think your best bet is to open an issue and ask: https://github.com/tpaviot/oce

Can second this!

However, I would recommend https://solvespace.com! It hits a sweet spot between features vs complexity/learning effort.

We use rebase on solvespace, along with sensible squashing so most commits along master are pretty self contained. You can see the clean history here:

https://github.com/solvespace/solvespace/commits/master/

I changed a behavior to the "more standard" one because it felt obviously right. This was a 3 line change. But the was enough backlash right there in the pull request. So I spent a couple hours remembering how to add a configuration option to keep the old way for those guys:

https://github.com/solvespace/solvespace/pull/1425

> If you like Linkage, you might also like Solvespace.

No, I mean Brent Curry's Linkage[1] bicycle design software, not David Rector's Linkage Mechanism Designer and Simulator[2].

You should read Wikipedia article.[0]

N.B. About SolveSpace, as I'm its experienced user[youtube,patreon], I may say next: yes, it could be used for bike mockup, as any other CAD, but it still has a lot of limitations and even does not export correct STEP files yet[3], and in FreeCAD such STEP could fixed only partially.[video]

So, for serious 3D CAD work I highly recommend use FreeCAD (and LibreCAD for 2D CAD work) instead of SolveSpace, and use SolveSpace only as a helper tool like a calc or as a notepad for noting ideas.

About Linkage Mechanism Designer and Simulator, it is only useful for planar (2D) kinematics analyze, and if You are looking an alternative for it take a look on Pyslvs[4], that is in part based on SolveSpace's solver.

[0] https://en.wikipedia.org/wiki/rattleCAD#History

[1] https://bikechecker.com/

[2] https://blog.rectorsquid.com/linkage-mechanism-designer-and-...

[3] https://github.com/solvespace/solvespace/issues/206

[4] https://github.com/KmolYuan/Pyslvs-UI

[video] https://www.youtube.com/watch?v=F3LJMeqUDrU

[youtube] https://www.youtube.com/@appsoft

[patreon] https://patreon.com/app4soft

C++ this file covers all the math for working with NURBS curves and surfaces:

https://github.com/solvespace/solvespace/blob/master/src/srf...

There is a lot more in other files - triangulation, booleans, creation - but the core math functions are there in very readable form.

>> Maybe somebody has statistical survey of how much of the existing deployed CPU core count is typically used?

My guess is very few cores are used on average. I did some testing with Solvespace to see which build options contributed most to performance:

https://github.com/solvespace/solvespace/issues/972

Obviously using OpenMP for multi-core was the big win. But what's not shown is that in typical usage (not the test I ran) if you're dragging some geometry around it will use all cores (in my case 4 cores / 8 threads) at about 50 percent utilization. That percentage probably drops as more cores are thrown at it due to Amdahl's Law. In other words, throwing double the cores at it will give a good boost to a lot of code that is already taking less than half the time (wall clock time, not CPU time).

We added OpenMP to a number of functions for significant performance gains. And in fact, any remining single-thread operation that gets the parallel treatment is likely to have a significant impact on overall performance since that is where most of the time is spent now. At this point we're more focused on features and bugs.

Algorithmic improvements are possible and I'd like to do those in the future, but they are much harder to do than sprinkling some #pragmas around critical loops. That will improve the scalability though, where multithreading really did not.