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⚛️ In React, rendering large lists or tables can be a drag on performance. The fix? Virtualization! the process of only rendering part of that large data set (just enough to fill the viewport). ✅ Improves rendering time significantly. ✅ Lowers memory consumption.

George Moller

119,062 subscribers

55,288 次观看 • 1 年前 •via X (Twitter)

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⚛️ I don't worry too much about re-renders in React. (unless I see performance issues) However, I do care about understanding how the rendering process works. ↓

⚛️ I don't worry too much about re-renders in React. (unless I see performance issues) However, I do care about understanding how the rendering process works. ↓

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Real-time rendering using #unity3d , part of the girl's face

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Before rendering// after rendering The animation edition this time😅🙃💀

Before rendering// after rendering The animation edition this time😅🙃💀

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EEVEE rendering can be so damn fast when the settings are set right #b3d

EEVEE rendering can be so damn fast when the settings are set right #b3d

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Here's the whole process step by step. I began to focus far more on construction than I had previously. It appears to save a significant amount of time when rendering.

Here's the whole process step by step. I began to focus far more on construction than I had previously. It appears to save a significant amount of time when rendering.

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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:

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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Bounded rendering in #ConjureSDF v0.3, a free update to support Blender 4.5+ Full release later this month. Speeds up rendering by only caring about pixels covered by the bounds of SDF groups. #blender3d #b3d #blender #3dart #3dmodeling

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Using the id modifier on subviews throws a wrench into List's optimization. Instead of just rendering what's visible, it forces all subviews to load instantly. To avoid this, build data sources that align with the Identifiable protocol.

Using the id modifier on subviews throws a wrench into List's optimization. Instead of just rendering what's visible, it forces all subviews to load instantly. To avoid this, build data sources that align with the Identifiable protocol.

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Another large speedup to SDF brick rendering (-20-40%) that is obvious in hindsight: When baking bricks, calculate the texel bounds that actually contain the surface, and then rasterize/ray-march just those bounds rather than the full brick bounds. 1/

Another large speedup to SDF brick rendering (-20-40%) that is obvious in hindsight: When baking bricks, calculate the texel bounds that actually contain the surface, and then rasterize/ray-march just those bounds rather than the full brick bounds. 1/

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Alright. I got the simulator rendering smoothly outside of the simulator app.

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Just a heads up that you should never be rendering 10,000 lines of anything, especially if all the lines are the same height, as in code. List virtualisation is a very old and simple technique. You can experiment with the below at

Just a heads up that you should never be rendering 10,000 lines of anything, especially if all the lines are the same height, as in code. List virtualisation is a very old and simple technique. You can experiment with the below at

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Even in the midst of an accident, they just can’t help themselves, instead of rendering aid or calling 911, they whip out their phones and hit record.

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