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SplatOverflow: Asynchronous Hardware Troubleshooting TL;DR: That's probably the most creative application of 3DGS I have ever seen :) Abstract (excerpt): SplatOverflow creates a novel boundary object, the SplatOverflow scene, that users reference to communicate about hardware. The scene comprises a 3D Gaussian Splat of the user’s hardware registered onto... show more

MrNeRF

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20,093 görüntüleme • 1 yıl önce •via X (Twitter)

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Paper:

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@josefprusa remote troubleshoot mechanical issues on a Prusa printer using 3D scanning :)

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Thanks for sharing!

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You are welcome!

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Agents have reached hardware. We are launching Flow v3, the Agentic Platform for Physical Engineering. We've spent over a year building it in secret, alongside the best hardware companies and AI research labs. An agent can now do real engineering work: change a requirement, push the update into your CAD and simulation tools, and flag every test that needs to rerun. Iterations/learning cycles that took months are being reduced to days. Agents are the biggest shift in how we engineer hardware since CAD. The core innovation for the CAD era was the parametric model. The core innovation for the Agentic Era is Flow's Systems Graph. The systems graph is a living model of every requirement, design model, test, analysis and every connection between them. It gives every agent the full context of the system, so every change stays consistent across the whole design. Engineers and agents work side by side on the same system. Engineers get to focus on architecture - the decisions that matter -while thousands of agents churn through rewriting reports, rerunning analysis and simulation, and triggering tests. Reusable rockets, self-driving cars, small modular reactors, robots that make decisions, the most complex machines ever built, are defined by millions of interconnected requirements, far beyond what any human team can keep aligned on its own. Rivian, Joby, Astranis, Skydio, Radiant, and the most ambitious hardware programs already build on Flow. More on the launch in the comments. Flow Engineering

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There's actually a lot of companies in East Asia that offer LiDAR hardware solutions alongside GS with BIM integration- CAD overlayed with well-calibrated / unwarped Gaussian Splats for aesthetic viewing. I seldom hear about these things in the US though

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In the Orbit of Solaris – A gaussian splat reconstruction of a scene from Tarkovsky’s masterpiece

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The largest advancement of the CUDA platform since its creation in 2006 is here 👀 Introducing CUDA Tile, a tile-based programming model that provides the ability to write algorithms at a higher level and abstract away the details of specialized hardware, such as tensor cores. Read the technical blog 👉

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Painting with 3D Gaussian Splat Brushes Contributions: • Designing a set of interactive tools and brush parameters for artistic brush creation and control using 3DGS content. • Computing oriented 3D Gaussian splat brushes for stamp-based painting on 3D surfaces represented as meshes or 3DGS scenes. • Deforming the splats in a brush stamp to ensure a smooth appearance of the painted 3D stroke. • Producing seamless brush strokes despite overlapping brush stamps, using diffusion inpainting. • Efficient modeling and rendering of brush strokes to facilitate 3DGS painting in real time.

Painting with 3D Gaussian Splat Brushes Contributions: • Designing a set of interactive tools and brush parameters for artistic brush creation and control using 3DGS content. • Computing oriented 3D Gaussian splat brushes for stamp-based painting on 3D surfaces represented as meshes or 3DGS scenes. • Deforming the splats in a brush stamp to ensure a smooth appearance of the painted 3D stroke. • Producing seamless brush strokes despite overlapping brush stamps, using diffusion inpainting. • Efficient modeling and rendering of brush strokes to facilitate 3DGS painting in real time.

MrNeRF

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Sequoia’s Shaun Maguire wrote a private hardware manifesto arguing that over the next 25 years, most of the money will be made in hardware: "Every software revolution is preceded by a hardware revolution." "To have the iOS App Store that enabled Uber, DoorDash, and all of these great companies - you needed to have the iPhone." "This AI revolution - we're seeing what it can do from the software layer, but it's still limited by hardware." "The hardware we were doing for a long time was all following Moore's Law. It was all branching out of this decision in the mid-1950s to go all in on the silicon supply chain." "That has created magic, and there's still a couple orders of magnitude of juice to squeeze, but we’re hitting fundamental physics limits - Dennard scaling, things like that." "I think this tech tree is branching into humanoid robots, into silicon photonics, into orbital data centers - all of these new hardware areas where there's going to be 20+ years of progress." "There's going to be incredible businesses built on the back of this. And a lot of dumpster fires."

TBPN

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hardware support should start at the device, not the ticket. your devices speak in logs, error codes, and packets. Telemetron listens, runs diagnostics, and automates troubleshooting so customers can get faster responses

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Shivani Patel

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I'm excited to share the Geo Register Plugin for LichtFeld Studio from the LichtFeld community! This plugin helps bring Gaussian splat scenes into real-world geographic space. It registers a scene to WGS-84 and ECEF coordinates, so you can click any point on the model and get its latitude, longitude and altitude. It supports multiple georeferencing sources, including EXIF GPS data, image position CSVs, RealityScan camera parameters and saved similarity transforms. Once the scene is registered, you can export geo-referenced splat models as LAS, LAZ or 3D Tiles datasets for use in GIS and 3D mapping workflows. Built for anyone working with drone data, photogrammetry, Gaussian splatting, GIS, ArcGIS or CesiumJS. Link in the comment below!

I'm excited to share the Geo Register Plugin for LichtFeld Studio from the LichtFeld community! This plugin helps bring Gaussian splat scenes into real-world geographic space. It registers a scene to WGS-84 and ECEF coordinates, so you can click any point on the model and get its latitude, longitude and altitude. It supports multiple georeferencing sources, including EXIF GPS data, image position CSVs, RealityScan camera parameters and saved similarity transforms. Once the scene is registered, you can export geo-referenced splat models as LAS, LAZ or 3D Tiles datasets for use in GIS and 3D mapping workflows. Built for anyone working with drone data, photogrammetry, Gaussian splatting, GIS, ArcGIS or CesiumJS. Link in the comment below!

MrNeRF

51,473 görüntüleme • 1 ay önce

SuperSplat's voxel collision is a game changer for 3D Gaussian splat creators. Generate a highly accurate, inescapable voxel representation of any splat, regardless of quality. Generation is performed on the GPU, so it's super-fast. The voxel representation is tiny - only 418KB for this scene. And all this magic is free and open source, courtesy of the splat-transform library from PlayCanvas.

SuperSplat's voxel collision is a game changer for 3D Gaussian splat creators. Generate a highly accurate, inescapable voxel representation of any splat, regardless of quality. Generation is performed on the GPU, so it's super-fast. The voxel representation is tiny - only 418KB for this scene. And all this magic is free and open source, courtesy of the splat-transform library from PlayCanvas.

Will Eastcott

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Embedder is the world's first hardware-aware coding agent. By understanding and interacting directly with your hardware, it achieves state of the art performance in an embedded systems (C++/Rust) context. Our latest update (v0.3.0) features a stunning new terminal UI, and our fastest, most capable firmware agent yet: Embedder

Embedder is the world's first hardware-aware coding agent. By understanding and interacting directly with your hardware, it achieves state of the art performance in an embedded systems (C++/Rust) context. Our latest update (v0.3.0) features a stunning new terminal UI, and our fastest, most capable firmware agent yet: Embedder

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Excited to introduce you to Seb, the coding agent for hardware. Seb is trained for low level systems in highly-regulated environments. Thank you to the engineers and companies that already trust Seb with their hardware. The feedback from hundreds of Firmware, Embedded, and Hardware Engineers has been invaluable. If you are building new hardware or updating a legacy system, Seb is here to help. Get started today at And as a thank you from the Root Access team -- the first 100 engineers to use promo code RAGE50 get 50% off their first month of Pro!

Excited to introduce you to Seb, the coding agent for hardware. Seb is trained for low level systems in highly-regulated environments. Thank you to the engineers and companies that already trust Seb with their hardware. The feedback from hundreds of Firmware, Embedded, and Hardware Engineers has been invaluable. If you are building new hardware or updating a legacy system, Seb is here to help. Get started today at And as a thank you from the Root Access team -- the first 100 engineers to use promo code RAGE50 get 50% off their first month of Pro!

Ryan Eppley

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Gabbit Robotics co-founder Nicole Mattero on why the next AI winners won't fight over your screen, they'll fight to get closer to you: She kicks things off with what she admits is a hot take: "My hot take is that hardware is the only moat going forward because anyone's going to be able to spin up a model and make it really good and really cheap." Her logic is simple. Once a strong model is cheap and easy for anyone to build, the model alone can't set you apart. So what actually protects you? "Where does that leave you? Basically companies that have tactile modes such as hardware are the only ones that actually survive." Hardware, though, is only one piece of her argument. The second piece is data: "The models that win in the long run are the ones where private data is their [moat]." From there, Mattero ties the two together. Hardware gives a company one moat. The ability to gather unique personal data gives it another. Put both in place and the edge starts to multiply: "When you have a company that has hardware as a [moat], but then B is able to collect really unique personal data, that also becomes a [moat]." And that, she says, is the reason so many foundation model companies are reaching beyond the screen and into the physical world: "That's why I think a lot of these foundation models are like, let's get something in the home or on the person. It gives them an edge on both fronts."

Gabbit Robotics co-founder Nicole Mattero on why the next AI winners won't fight over your screen, they'll fight to get closer to you: She kicks things off with what she admits is a hot take: "My hot take is that hardware is the only moat going forward because anyone's going to be able to spin up a model and make it really good and really cheap." Her logic is simple. Once a strong model is cheap and easy for anyone to build, the model alone can't set you apart. So what actually protects you? "Where does that leave you? Basically companies that have tactile modes such as hardware are the only ones that actually survive." Hardware, though, is only one piece of her argument. The second piece is data: "The models that win in the long run are the ones where private data is their [moat]." From there, Mattero ties the two together. Hardware gives a company one moat. The ability to gather unique personal data gives it another. Put both in place and the edge starts to multiply: "When you have a company that has hardware as a [moat], but then B is able to collect really unique personal data, that also becomes a [moat]." And that, she says, is the reason so many foundation model companies are reaching beyond the screen and into the physical world: "That's why I think a lot of these foundation models are like, let's get something in the home or on the person. It gives them an edge on both fronts."

Big Brain AI

19,195 görüntüleme • 1 ay önce

Watch Nvidia CEO Jensen Huang reveal what he described as the state of the art in AI hardware: the new Rubin computing architecture. “Vera Rubin is designed to address this fundamental challenge that we have: The amount of computation necessary for AI is skyrocketing.” Huang said. “Today, I can tell you that Vera Rubin is in full production.” According to Nvidia’s tests, Rubin architecture operates three and a half times faster than the previous Blackwell architecture on model-training tasks and five times faster on inference tasks, and that's just the start.

Watch Nvidia CEO Jensen Huang reveal what he described as the state of the art in AI hardware: the new Rubin computing architecture. “Vera Rubin is designed to address this fundamental challenge that we have: The amount of computation necessary for AI is skyrocketing.” Huang said. “Today, I can tell you that Vera Rubin is in full production.” According to Nvidia’s tests, Rubin architecture operates three and a half times faster than the previous Blackwell architecture on model-training tasks and five times faster on inference tasks, and that's just the start.

TechCrunch

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