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#computergraphics

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Assn for Computing Machinery

As 3D generative AI matures, it’s reshaping creativity across multiple disciplines.

This year, ever-expanding work in the 3D generative AI space will be explored as part of the SIGGRAPH Technical Papers program, including these three novel methods — each offering a unique take on how 3D generative AI is being applied.

Check out the award-winning research here: blog.siggraph.org/2025/06/sigg

#SIGGRAPH2025#TechnicalPapers#Research
claude

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.

Hacker News

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.
#HackerNews#ProtoGS#3DRendering
Codrops

When matsuoka-601 dropped his WebGPU fluid simulation demos, they left everyone in awe—pushing the limits of what's possible in the browser with breathtaking realism and incredible performance. 💦

Now, he's been kind enough to take us behind the scenes, breaking down the magic behind the mesmerizing waves, high-performance physics, and real-time rendering!

Read it here: tympanus.net/codrops/2025/02/2

#webgpu#fluidsimulation#realtimerendering
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