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Today we're announcing microSLAM, a monocular SLAM system built by our computer vision team in Zürich with ETH Zurich's Computer Vision and Geometry Lab. It ranks first on the LaMaria benchmark for monocular SLAM, and holds up against systems that carry many more cameras and IMUs. Ours runs on... a single RGB stream. Robot learning is bottlenecked on data, and the largest untapped source is people going about their work. A camera on someone's head for an afternoon in a plant is a record of how that plant actually operates, including all the parts nobody writes down. That data is only useful if you can recover the geometry, and geometry from one moving camera in a world that will not sit still is the hard version of the problem. Solving it monocular is what makes the data cheap. Rigs do not scale to every worker. Glasses do. microSLAM turns that footage into interactive 3D environments where behavioral foundation models can be trained. One person walks a factory floor. A robot learns to navigate it.show more

Gianni Hodel

1,159 subscribers

67,975 次观看 • 13 天前 •via X (Twitter)

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20 条评论

Pablo Vela 的头像

Pablo Vela13 天前

Cool! What’s the runtime compared to orbslam 3?

toolshed 的头像

toolshed13 天前

The extra cameras and IMUs are not only about accuracy. One moving camera recovers geometry up to an unknown scale factor, and a stereo baseline or an accelerometer is what gets the metres back. A robot judging a 40cm gap needs metres. Where does microSLAM get its scale?

/ 的头像

/13 天前

closed source?

Ricardo de Azambuja 的头像

Ricardo de Azambuja13 天前

Link to the repo or it doesn't exist 🙃

mansin 的头像

mansin13 天前

Awesome! Would love to test it

Azeem 的头像

Azeem13 天前

microSLAM uses a single RGB stream and still outperforms systems with multiple cameras and IMUs. The LaMaria benchmark is one of the most rigorous for monocular SLAM. The fact that ETH Zurich's lab is involved adds significant credibility.

Vasco 🇳🇱🇹🇭 的头像

Vasco 🇳🇱🇹🇭13 天前

👀👀👀

The Canaanite 的头像

The Canaanite13 天前

Wonderful, wen robot for housework and how much?

Armeen 的头像

Armeen13 天前

The hard part is not monocular geometry; it is keeping it stable under motion and layout change. We can help capture the rare failure slices, then keep #heldout evaluation on unseen plant configurations rather than another pass through the same walk.

AI Mastery Guide 的头像

AI Mastery Guide13 天前

Ranking first with one camera, impressive

indoor positioning 的头像

indoor positioning13 天前

开源没

Senthilnathan K 的头像

Senthilnathan K13 天前

Would love to test this and integrate into our platform, monocular depth estimation if done accurately is super useful

Alvaro L 的头像

Alvaro L13 天前

How does it compare to Lingbot Map

DAY☀️ 的头像

DAY☀️13 天前

love a low calorie solution

Daniel Mika 的头像

Daniel Mika13 天前

Cool work guys

Aryan Dhawan 的头像

Aryan Dhawan13 天前

Getting a first-place result from one camera is honestly exciting. I’d look next at motion blur and fast body rotation, since those are exactly where monocular SLAM gets uncomfortable on a walking robot.

remco_bos 的头像

remco_bos12 天前

Release source and open source MicroRover

Julien Blanchon 🇺🇦 的头像

Julien Blanchon 🇺🇦13 天前

Link ?

Roko’s Basilica 的头像

Roko’s Basilica13 天前

Cool, but do you really need to use AI to write a slop tweet?

Dredge 的头像

Dredge12 天前

Monocular SLAM ranks first on LaMaria - benchmark conditions. Real factory: uncontrolled light, fast movements, occlusions. Does geometry recovery hold? If microSLAM degrades 10-20% on messy data, how much does that hurt downstream model training?

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Dustin

64,593 次观看 • 6 个月前

A one-man trading hedge fund now costs about $100 a month in AI subscriptions. Five PhDs on a Citadel desk cost $270,000 a month, and that is the cheap end. That buys one tested idea a month. Man Group's AI writes hundreds of new signals a week, on a floor where the humans used to manage twenty in a quarter. Fifty AI agents do the reading and the writing. One of them exists only to destroy what the others build, and that one is the reason any of it works. Every job on that payroll is now an agent you can run yourself: > AI research agents reading filings, transcripts, options flow and on-chain data overnight, fifty personas at once, none of them allowed to see what the crowd is pricing > coding agent that turns one sentence of plain English into entry rules, exit rules, sizing and risk limits, then debugs itself until it runs > backtest agent that replays five years of that idea in about 12 seconds > breaker agent, an AI whose only job is killing the thing, at double the trading costs and in the ten worst markets that asset has ever seen > critic agent that reads your journal and names the mistake you keep making Nobody on that list asks for a bonus in January. $200 million is already run this way on one platform where 52,000 people mostly sit and watch the machine work. The full map of what is built and what is missing is in the article below. Bookmark & read it If you're not yet using AI in your trading and investing.

A one-man trading hedge fund now costs about $100 a month in AI subscriptions. Five PhDs on a Citadel desk cost $270,000 a month, and that is the cheap end. That buys one tested idea a month. Man Group's AI writes hundreds of new signals a week, on a floor where the humans used to manage twenty in a quarter. Fifty AI agents do the reading and the writing. One of them exists only to destroy what the others build, and that one is the reason any of it works. Every job on that payroll is now an agent you can run yourself: > AI research agents reading filings, transcripts, options flow and on-chain data overnight, fifty personas at once, none of them allowed to see what the crowd is pricing > coding agent that turns one sentence of plain English into entry rules, exit rules, sizing and risk limits, then debugs itself until it runs > backtest agent that replays five years of that idea in about 12 seconds > breaker agent, an AI whose only job is killing the thing, at double the trading costs and in the ten worst markets that asset has ever seen > critic agent that reads your journal and names the mistake you keep making Nobody on that list asks for a bonus in January. $200 million is already run this way on one platform where 52,000 people mostly sit and watch the machine work. The full map of what is built and what is missing is in the article below. Bookmark & read it If you're not yet using AI in your trading and investing.

cvxv666

173,490 次观看 • 1 个月前

🌟 Today we’re sharing the vision for a New Web3 — a more interconnected, human internet that drives new value systems and can lay the foundation for a new digital society. Here is the first of two articles, detailing the journey of our evolution and why we built new standards. This one is for the visionaries and dreamers of the initial Ethereum vision of a decentralised internet and shows how we can evolve it into a universal decentralized operating system, entailing the whole of web3. This one is slightly more technical. 👉 Read on Medium:

🌟 Today we’re sharing the vision for a New Web3 — a more interconnected, human internet that drives new value systems and can lay the foundation for a new digital society. Here is the first of two articles, detailing the journey of our evolution and why we built new standards. This one is for the visionaries and dreamers of the initial Ethereum vision of a decentralised internet and shows how we can evolve it into a universal decentralized operating system, entailing the whole of web3. This one is slightly more technical. 👉 Read on Medium:

LUKSO

93,954 次观看 • 1 年前

Have you ever seen a floor that is raised above the concrete floor? That is called a raised floor or raised access floor. It creates space beneath for electrical and data cables, internet lines, and other building services. The biggest advantage is flexibility. For example, in an office, data center, or bank, you may need to move cables or add new ones in the future. With a raised floor, you can lift the access panel and work underneath without breaking the floor. This is why they are commonly useful in: Data centers, large offices, banks, control rooms, telecom buildings, laboratories, and other buildings with many services. And yes, a raised floor can be very strong. But don't assume that every raised floor can carry heavy loads. The panels, pedestals, supports, and spacing must be designed based on the items to be placed on the floor. A data center with heavy server racks will require a different specification from a normal office. So, should every building have a raised floor? No. For a simple residential building with very few services, it may just be an unnecessary extra cost. The point is simple: Don't use a construction system because it looks modern. Use it because it solves a problem. When you have many services, frequent changes, and a need for easy maintenance, a raised floor can be a very smart solution. Good architecture is knowing where to use it and where not to.

Have you ever seen a floor that is raised above the concrete floor? That is called a raised floor or raised access floor. It creates space beneath for electrical and data cables, internet lines, and other building services. The biggest advantage is flexibility. For example, in an office, data center, or bank, you may need to move cables or add new ones in the future. With a raised floor, you can lift the access panel and work underneath without breaking the floor. This is why they are commonly useful in: Data centers, large offices, banks, control rooms, telecom buildings, laboratories, and other buildings with many services. And yes, a raised floor can be very strong. But don't assume that every raised floor can carry heavy loads. The panels, pedestals, supports, and spacing must be designed based on the items to be placed on the floor. A data center with heavy server racks will require a different specification from a normal office. So, should every building have a raised floor? No. For a simple residential building with very few services, it may just be an unnecessary extra cost. The point is simple: Don't use a construction system because it looks modern. Use it because it solves a problem. When you have many services, frequent changes, and a need for easy maintenance, a raised floor can be a very smart solution. Good architecture is knowing where to use it and where not to.

Arcfunmi

69,928 次观看 • 17 天前

Want to create an avatar from a single image? FlexAvatar is a transformer model that creates full 360°, high-quality, and expressive 3D head avatar from just a single portrait image in minutes. Real-time Demo: FlexAvatar's lightweight architecture allows both animation and rendering in real-time, enabling interactive user experiences. To create a new 3D head avatar, only one image is required, e.g., from a webcam. The final avatar is ready after 2 minutes. Architecture: Under the hood, FlexAvatar adopts a transformer-based encoder-decoder design. The encoder maps the input image onto a latent avatar space, while the decoder produces 3D Gaussian attribute maps by incorporating the animation signal via cross-attention. The model learns all facial animations directly from the data without relying on pre-built 3D face models. This equips the avatars with realistic facial expressions. The internal avatar latent space can be conveniently used to integrate additional observations of a person via fitting. This enables use-cases where more than one image of a person is available, e.g., from a phone scan of the person. We train jointly on 2D monocular videos and multi-view data. However, in monocular videos, the animation signal leaks the target viewpoint, causing the model to produce incomplete 3D heads. We call this phenomenon entanglement of driving signal and target viewpoint. To prevent entanglement, we introduce bias sinks. These are learnable tokens that indicate whether a training sample stems from a monocular or a multi-view dataset. During training, the model learns to produce incomplete 3D heads only when the monocular token is present. During inference, FlexAvatar then always uses the multi-view token for which the model has learned to produce complete 3D heads. This simple design allows to combine the generalizability from monocular data with the quality of multi-view data. FlexAvatar summary: - Input: Single-image, phone scan, or monocular video - Output: Full 360° head avatar - Expressive animations - Real-time rendering and animation - Generalization to any portrait - Create a new avatar in 2 minutes - Use bias sinks to combine 2D and 3D data 🏠 🌍 🎥 Great work by Tobias Kirschstein and Simon Giebenhain!

Matthias Niessner

96,371 次观看 • 9 个月前

WOW. 😳 Apple just quietly won the 3D maps war at WWDC. Gaussian Splatting is coming to Apple Maps Flyover this fall. Apple Maps Flyover covers 300+ cities. Until yesterday, every single one was built on standard drone photogrammetry. The technology captures photos from the air and reconstructs 3D geometry from them. Gaussian Splatting does not reconstruct geometry. It represents the scene as millions of tiny 3D ellipsoids, each one carrying its own color and opacity information based on how light actually behaves in that location. The output is not a mesh model. It is a field of light. When you move through it, it does not crumble at the edges. The detail holds because it was never geometry to begin with. Apple has been hiring for this for years. Their SHARP model, published in research last year, generates photorealistic 3D scenes from a single image in under a second. Google has more sensor data than anyone. More Street View cars, more satellites, more capture history. On navigation accuracy and geodata depth, Google Maps is still ahead by most measures. But fidelity in 3D city rendering is a different competition, and Apple just set a bar in that. Most people will experience this in the fall without knowing the name of the technology. They will open Flyover, look at a city they know, and notice it looks different. Real, not rendered. That is the moment Gaussian Splatting stops being a research term and becomes something a billion people use. Bookmark this. It will look prescient by October.

WOW. 😳 Apple just quietly won the 3D maps war at WWDC. Gaussian Splatting is coming to Apple Maps Flyover this fall. Apple Maps Flyover covers 300+ cities. Until yesterday, every single one was built on standard drone photogrammetry. The technology captures photos from the air and reconstructs 3D geometry from them. Gaussian Splatting does not reconstruct geometry. It represents the scene as millions of tiny 3D ellipsoids, each one carrying its own color and opacity information based on how light actually behaves in that location. The output is not a mesh model. It is a field of light. When you move through it, it does not crumble at the edges. The detail holds because it was never geometry to begin with. Apple has been hiring for this for years. Their SHARP model, published in research last year, generates photorealistic 3D scenes from a single image in under a second. Google has more sensor data than anyone. More Street View cars, more satellites, more capture history. On navigation accuracy and geodata depth, Google Maps is still ahead by most measures. But fidelity in 3D city rendering is a different competition, and Apple just set a bar in that. Most people will experience this in the fall without knowing the name of the technology. They will open Flyover, look at a city they know, and notice it looks different. Real, not rendered. That is the moment Gaussian Splatting stops being a research term and becomes something a billion people use. Bookmark this. It will look prescient by October.

Shruti

19,832 次观看 • 3 个月前

SMITH: "Many of the people who are in [PM Carney's] caucus still hold on to the same views that they have for the last 10 years. That Alberta is a problem, our economic wealth is a problem, developing it is a problem, so keep it in the ground. And that is not going to happen."

SMITH: "Many of the people who are in [PM Carney's] caucus still hold on to the same views that they have for the last 10 years. That Alberta is a problem, our economic wealth is a problem, developing it is a problem, so keep it in the ground. And that is not going to happen."

Scott Robertson

111,142 次观看 • 7 个月前

Mark Zuckerberg just described the obsolescence of every institution on Earth and delivered it like a product update. Zuckerberg: “I just think in the future almost everyone is gonna have the power of a 10,000-person organization.” He did not say better tools. He did not say smarter apps. He said the full cognitive output of ten thousand human beings. Packaged into a product. Handed to one person. That is not an upgrade. That is the end of the reason human beings organize at all. Companies exist because one brain is not enough. Governments exist because coordination requires hierarchy. Universities exist because knowledge demands infrastructure. Every institution ever built was a workaround for the same limitation. No single person could do it alone. Zuckerberg is telling you that limitation is about to disappear. The 500-person startup becomes one founder and an AI stack. The law firm becomes one attorney and a system that never sleeps. The hospital becomes one doctor carrying every specialist in their pocket. That is not speculation. That is a deployment schedule. And the man writing it runs a 70,000-person company. He employs 70,000 people and just told the world one person will soon need none of them. That is not a prediction. That is a confession from the man who will be first to act on it. But the part nobody is discussing matters more. This technology does not land on everyone equally. It lands first on the people who already command 10,000-person organizations. Zuckerberg does not get the power of 10,000 people. He already has that. He gets the power of 10,000 organizations. Every revolution in history was sold as liberation. The printing press was supposed to democratize knowledge. It built media empires. The internet was supposed to democratize commerce. It built trillion-dollar platforms. The tool always arrives as liberation. It always settles as leverage. And leverage always consolidates upward. Zuckerberg is not wrong about the capability. One person will do what ten thousand once did. But the question nobody is asking is the only one that matters. If everyone wields the output of 10,000 people, what is a single person actually worth? And then Zuckerberg answered his own question without realizing it. Zuckerberg: “If the intelligence of a 10,000-person company is not greater than the intelligence of a single person, then what are we doing here?” He meant it as a case for AI. That is the most brutal thing a CEO has ever said about the people who work for him.

Mark Zuckerberg just described the obsolescence of every institution on Earth and delivered it like a product update. Zuckerberg: “I just think in the future almost everyone is gonna have the power of a 10,000-person organization.” He did not say better tools. He did not say smarter apps. He said the full cognitive output of ten thousand human beings. Packaged into a product. Handed to one person. That is not an upgrade. That is the end of the reason human beings organize at all. Companies exist because one brain is not enough. Governments exist because coordination requires hierarchy. Universities exist because knowledge demands infrastructure. Every institution ever built was a workaround for the same limitation. No single person could do it alone. Zuckerberg is telling you that limitation is about to disappear. The 500-person startup becomes one founder and an AI stack. The law firm becomes one attorney and a system that never sleeps. The hospital becomes one doctor carrying every specialist in their pocket. That is not speculation. That is a deployment schedule. And the man writing it runs a 70,000-person company. He employs 70,000 people and just told the world one person will soon need none of them. That is not a prediction. That is a confession from the man who will be first to act on it. But the part nobody is discussing matters more. This technology does not land on everyone equally. It lands first on the people who already command 10,000-person organizations. Zuckerberg does not get the power of 10,000 people. He already has that. He gets the power of 10,000 organizations. Every revolution in history was sold as liberation. The printing press was supposed to democratize knowledge. It built media empires. The internet was supposed to democratize commerce. It built trillion-dollar platforms. The tool always arrives as liberation. It always settles as leverage. And leverage always consolidates upward. Zuckerberg is not wrong about the capability. One person will do what ten thousand once did. But the question nobody is asking is the only one that matters. If everyone wields the output of 10,000 people, what is a single person actually worth? And then Zuckerberg answered his own question without realizing it. Zuckerberg: “If the intelligence of a 10,000-person company is not greater than the intelligence of a single person, then what are we doing here?” He meant it as a case for AI. That is the most brutal thing a CEO has ever said about the people who work for him.

Dustin

54,061 次观看 • 4 个月前