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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.

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George Moller

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55,288 просмотров • 1 год назад •via X (Twitter)

Наука и технологии
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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.
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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.

Dariusz Kieliszek

15,522 просмотров • 1 год назад

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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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:

Shikun Liu

22,471 просмотров • 11 месяцев назад

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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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.

fatbobman (东坡肘子)

46,087 просмотров • 1 год назад

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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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

João Desager

13,770 просмотров • 11 месяцев назад

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.
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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

39,022 просмотров • 2 лет назад

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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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.

MrNeRF

62,564 просмотров • 1 год назад

React Server Components bring rendering flexibility to the component level in Next.js. Hear from Aurora Scharff at Next.js Conf to learn about: • Async components • Efficient data management • Optimizing load times and interactivity Join us in SF or stream online on Oct 24.
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React Server Components bring rendering flexibility to the component level in Next.js. Hear from Aurora Scharff at Next.js Conf to learn about: • Async components • Efficient data management • Optimizing load times and interactivity Join us in SF or stream online on Oct 24.

Next.js

16,259 просмотров • 1 год назад

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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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

Nic Barker

268,322 просмотров • 4 месяцев назад

Visualizing complex data in Python just got easier! Meet Cosmograph for Python 🪐: The widget brings GPU-accelerated, interactive layout graph rendering right inside your Jupyter notebooks. Here’s why it’s a game-changer: ⚡ GPU-accelerated performance ⛓️ Interactive network exploration with pan, zoom, hover & selection ⚙️ Rich configuration APIs for layout, color, size & more 📦 Seamless notebook integration & easy Python installation But that’s not all: ✅ Force-directed simulations for dynamic layouts ✅ Smooth handling of large-scale networks ✅ Minimal setup—just pip install cosmograph Link to the repo in next tweet! ______ Follow us → Daily Dose of Data Science ✔️ For more insights & tutorials on AI and Machine Learning.
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Visualizing complex data in Python just got easier! Meet Cosmograph for Python 🪐: The widget brings GPU-accelerated, interactive layout graph rendering right inside your Jupyter notebooks. Here’s why it’s a game-changer: ⚡ GPU-accelerated performance ⛓️ Interactive network exploration with pan, zoom, hover & selection ⚙️ Rich configuration APIs for layout, color, size & more 📦 Seamless notebook integration & easy Python installation But that’s not all: ✅ Force-directed simulations for dynamic layouts ✅ Smooth handling of large-scale networks ✅ Minimal setup—just pip install cosmograph Link to the repo in next tweet! ______ Follow us → Daily Dose of Data Science ✔️ For more insights & tutorials on AI and Machine Learning.

Daily Dose of Data Science

27,283 просмотров • 1 год назад

the go-to method for data exfil after a successful prompt injection is rendering an image or a clickable link that's why m365 copilot refuses to print links no matter what unless of course..
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the go-to method for data exfil after a successful prompt injection is rendering an image or a clickable link that's why m365 copilot refuses to print links no matter what unless of course..

Michael Bargury

23,461 просмотров • 2 лет назад

3D Gaussian Splatting for Real-Time Radiance Field Rendering paper page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.
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3D Gaussian Splatting for Real-Time Radiance Field Rendering paper page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.

AK

634,096 просмотров • 3 лет назад

an important optim for any isometric RTS: split the map into sectors, and do frustum culling. I know that Unity already has great culling, but it feel wrong to only rely on Unity for it, what if we want to have huge maps? Of course, logic and rendering has to be completely decoupled, because entities in invisible sectors still need to "think". And of course, I broke the minimap because all sectors must be visible in it at all times. But it's fixable by manipulating visibility though rendering layers instead of mesh renderer state.
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an important optim for any isometric RTS: split the map into sectors, and do frustum culling. I know that Unity already has great culling, but it feel wrong to only rely on Unity for it, what if we want to have huge maps? Of course, logic and rendering has to be completely decoupled, because entities in invisible sectors still need to "think". And of course, I broke the minimap because all sectors must be visible in it at all times. But it's fixable by manipulating visibility though rendering layers instead of mesh renderer state.

Amine Rehioui

62,604 просмотров • 10 месяцев назад

This is a rendering video of the white version of the Galaxy S26 Ultra I made using Seedance 2.0. You can see that although it is not perfect, it has been done very well. I believe that by Seedance 4.0, it will be close to perfect.
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This is a rendering video of the white version of the Galaxy S26 Ultra I made using Seedance 2.0. You can see that although it is not perfect, it has been done very well. I believe that by Seedance 4.0, it will be close to perfect.

Ice Universe

77,535 просмотров • 7 месяцев назад

🚫 𝗔𝘃𝗼𝗶𝗱 Using a full context value in every component can trigger unnecessary re-renders. ✅ 𝗜𝗻𝘀𝘁𝗲𝗮𝗱 Use 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘴𝘦𝘭𝘦𝘤𝘵𝘰𝘳𝘴 to pull only the data you need. This reduces re-renders and improves performance.
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🚫 𝗔𝘃𝗼𝗶𝗱 Using a full context value in every component can trigger unnecessary re-renders. ✅ 𝗜𝗻𝘀𝘁𝗲𝗮𝗱 Use 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘴𝘦𝘭𝘦𝘤𝘵𝘰𝘳𝘴 to pull only the data you need. This reduces re-renders and improves performance.

George Moller

52,973 просмотров • 1 год назад

DLSS 5 is available now in NBA 2K27 with 3D-Guided Neural Rendering! I have spent a lot of time playing with DLSS 5 on and the jump in realism is huge. 🏀
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DLSS 5 is available now in NBA 2K27 with 3D-Guided Neural Rendering! I have spent a lot of time playing with DLSS 5 on and the jump in realism is huge. 🏀

Jacob Freeman

11,486 просмотров • 20 дней назад

Throwback from when I built a water rendering feature that made the ocean sparkle all the way to the horizon, that perfect shimmering path under the sun. Still one of my favorite “it finally works” moments in graphics. Proud of that shader magic!
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Throwback from when I built a water rendering feature that made the ocean sparkle all the way to the horizon, that perfect shimmering path under the sun. Still one of my favorite “it finally works” moments in graphics. Proud of that shader magic!

Horizonite

35,165 просмотров • 5 месяцев назад

I'm building a renderer that uses very small voxels and displacement mapping to modernize the retro aesthetic of games like Doom and Quake. More info in thread #voxels #gamedev #rendering
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I'm building a renderer that uses very small voxels and displacement mapping to modernize the retro aesthetic of games like Doom and Quake. More info in thread #voxels #gamedev #rendering

Daniel Schroeder

169,158 просмотров • 2 лет назад