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Sparse allocation/rendering of SDF bricks is now working! The pipeline is fully GPU-driven - a series of compute shaders that manages brick allocation/baking in a 3D texture atlas, followed by draw-indirect for rendering. Still a lot more work to do, including automatic LOD.

Mike Turitzin

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39,022 次观看 • 2 年前 •via X (Twitter)

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Got the BVH (AABB tree) working with a nice visualization. (The circles and arrows show the hierarchy of the tree.) This is all CPU-side for now, so next is to ship the BVH to the GPU for accelerating SDF brick baking. It's already in a flat buffer so that is straightforward.

Got the BVH (AABB tree) working with a nice visualization. (The circles and arrows show the hierarchy of the tree.) This is all CPU-side for now, so next is to ship the BVH to the GPU for accelerating SDF brick baking. It's already in a flat buffer so that is straightforward.

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Getting a bunch of questions about this! It's a full gpu rendering pipeline with built in shader editor for Rive, which can interleave 2D + 3D. Think WebGPU (minus compute, for now) that runs anywhere, not just the browser. You can implement anything at all. Cell shading. Background blurs. Deformation effects. And yes, even Rive vector components as textures. Shaders are precompiled and ship with your .riv. Minimal runtime size impact. Still WIP.

Getting a bunch of questions about this! It's a full gpu rendering pipeline with built in shader editor for Rive, which can interleave 2D + 3D. Think WebGPU (minus compute, for now) that runs anywhere, not just the browser. You can implement anything at all. Cell shading. Background blurs. Deformation effects. And yes, even Rive vector components as textures. Shaders are precompiled and ship with your .riv. Minimal runtime size impact. Still WIP.

Guido Rosso

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Just the example itself is so fun to play with! 🔆 I can't wait to pull the code and swap the rendering pipeline to achieve even more cool effects. This is the dream of TypeGPU, AI inference, complex compositing, all on the GPU in a seamless, type-safe pipeline.

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Iwo Plaza | TypeGPU

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Realistic hair and fur rendering in EEVEE, is that of any interest to you as a 3d artist? 🤭 #b3d

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Is Google taking initial steps to enhance Street View? For some reason, Street View seems stuck in technology that feels outdated. I wonder if we'll see such improvements on the product side. Also, note how much better it performs in all aspects compared to Zip-NeRF in their presented material. It offers more details and fewer artifacts. Great work! "LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering" Contributions: • We propose a novel LOD representation for 3DGS which, unlike previous methods [27, 28, 17], does not recompute the list of used Gaussians at each frame. This allows for acceleration and compaction, enabling the rendering of large-scale scenes even on mobile devices. • We design a strategy to automatically select optimal hyperparameters for splitting LODs, whereas most other methods require manual tuning of hyperparameters for each 3D scene. • To further accelerate rendering, we split the scene into chunks and pre-compute sets of active Gaussians per chunk. • Finally, we introduce a novel opacity interpolation scheme to produce visually pleasing rendering and eliminate artifacts when transitioning between chunks.

Is Google taking initial steps to enhance Street View? For some reason, Street View seems stuck in technology that feels outdated. I wonder if we'll see such improvements on the product side. Also, note how much better it performs in all aspects compared to Zip-NeRF in their presented material. It offers more details and fewer artifacts. Great work! "LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering" Contributions: • We propose a novel LOD representation for 3DGS which, unlike previous methods [27, 28, 17], does not recompute the list of used Gaussians at each frame. This allows for acceleration and compaction, enabling the rendering of large-scale scenes even on mobile devices. • We design a strategy to automatically select optimal hyperparameters for splitting LODs, whereas most other methods require manual tuning of hyperparameters for each 3D scene. • To further accelerate rendering, we split the scene into chunks and pre-compute sets of active Gaussians per chunk. • Finally, we introduce a novel opacity interpolation scheme to produce visually pleasing rendering and eliminate artifacts when transitioning between chunks.

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Some things don't need to be complicated. GPU compute is one of them. Pay only for what you use, nothing more. Find a GPU that works for you today:

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What was a complex hacky pipeline in 2023 to take indoor 3d scans and reskin them to different types of decor is now just a few clicks in 2025. World labs marble has collapsed a lot of the complexity involved in generating and editing 3d worlds:

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Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

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Some performance optimizations were done in MW3 now manges to reach 60 FPS or even more, while being fully sandboxed. Still a lot to optimize, but this is a major milestone. The game is playable now 🥳

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