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#computergraphics

4 posts4 participants0 posts today

Doing some experiments with #RayTracing #ComputerGraphics using #Bonzomatic #GLSL #shader editor.

I define a 24-cell as the intersection of 24 half-spaces in 4D (with normals all permutations of (±1,±1,0,0)), then slice through constant 4th coordinate to get a 3D object, and simulate it as a glass-like material with reflection and refraction.

Future enhancement ideas:

- handle polarised light properly (currently I ignore polarisation, simply averaging the Fresnel reflection coefficients)

- wavelength-dependent index of refraction and absorption

- anti-aliasing (currently the edges are steppy as there is only 1 sample per pixel in a regular grid)

- improve efficiency (internal ray bounce is O(N^2) where N is the number of surfaces, could probably be O(N) with some extra maths insights)

- do 4D->3D slicing on CPU instead of every ray bounce

- add other 4D shapes

#Polarisation is something I haven't done before, so I'm curious to see how to implement it and how it changes appearance.

I stumbled upon an interesting blog today while searching for an image of the Silicon Graphics 3D cube logo. Named "Abort Retry Fail" by Bradford Morgan White, the blog's articles document computer history. I eventually wound up reading three of them.

#1 "The Rise and Fall of Silicon Graphics"

abortretry.fail/p/the-rise-and

Abort Retry Fail · The Rise and Fall of Silicon GraphicsBy Bradford Morgan White

ProtoGS: Efficient and High-Quality Rendering with 3D Gaussian Prototypes

arxiv.org/abs/2503.17486

arXiv.orgProtoGS: Efficient and High-Quality Rendering with 3D Gaussian Prototypes3D Gaussian Splatting (3DGS) has made significant strides in novel view synthesis but is limited by the substantial number of Gaussian primitives required, posing challenges for deployment on lightweight devices. Recent methods address this issue by compressing the storage size of densified Gaussians, yet fail to preserve rendering quality and efficiency. To overcome these limitations, we propose ProtoGS to learn Gaussian prototypes to represent Gaussian primitives, significantly reducing the total Gaussian amount without sacrificing visual quality. Our method directly uses Gaussian prototypes to enable efficient rendering and leverage the resulting reconstruction loss to guide prototype learning. To further optimize memory efficiency during training, we incorporate structure-from-motion (SfM) points as anchor points to group Gaussian primitives. Gaussian prototypes are derived within each group by clustering of K-means, and both the anchor points and the prototypes are optimized jointly. Our experiments on real-world and synthetic datasets prove that we outperform existing methods, achieving a substantial reduction in the number of Gaussians, and enabling high rendering speed while maintaining or even enhancing rendering fidelity.

So, that #FANuary thing sounds like a cool idea. Maybe I have one or two mentions to contribute as well.

So here is a shout-out to @acegikmo, who I initially found through her excellent deep dive into the math behind splines youtu.be/jvPPXbo87ds, which is amazingly detailed and illustrated.
Additionally, she develops quite a few cool tools, mostly around #GameDev and #ComputerGraphics (where she also shows the wonderful weirdness when things go wrong).
Take a look yourselves!

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