

I’ll need to give this a read, but I’m not super sure what’s novel here. The core idea sounds a lot like GaussianImage (ECCV '24), in which they basically perform 3DGS except with 2D gaussians to fit an image with fewer parameters than implicit neural methods. Thanks for the breakdown!
You’re correct about all of this, but it’s way easier to press print than machine a part from stock. I do some machining as well (I don’t own the machines, but I’m trained on the mill, lathe, and waterjet in our shop). So most of the time if I can get away with a 3d printed part, it’s worth it for the time savings alone. Plus sometimes the easiest or optimal geometry to design is not something that can be machined, but can be printed.
It’s specific circumstances where the basic filaments fall short, like creep and heat resistance, irrespective of print parameters. ASA and PET-CF work well in most of these spots, so I don’t do anything more exotic.