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Gaussian splats look impressive—but robotics needs more than photorealism. Niantic Spatial generates an aligned collision mesh alongside every Gaussian splat, creating machine-readable digital twins with accurate geometry, depth, and collision awareness. Built for Spatial AI, Physical AI, and simulation in NVIDIA Robotics Isaac Sim. Learn more: #GaussianSplats #SpatialAI #PhysicalAI... show more

Niantic Spatial 🌎

957,642 subscribers

54,070 次观看 • 1 个月前 •via X (Twitter)

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7 条评论

ResinBody 的头像

ResinBody1 个月前

How does the collision mesh handle thin structures like railings or cables? They can look fine in the splat and still be hard to represent as usable geometry.

arpu 的头像

arpu1 个月前

is it opensource?

worksrecorded.com - AI news, construction 的头像

worksrecorded.com - AI news, construction1 个月前

For jobsite robots, the collision mesh also needs an operational confidence layer: capture age, occlusion zones, tolerance by object class, and a clear invalidation rule after site changes. Geometry is actionable only when the robot knows where it may be wrong.

Ian Smith 的头像

Ian Smith1 个月前

👀

Geωrge 的头像

Geωrge1 个月前

I love this technology... It's amazing

Pratik Sharda 的头像

Pratik Sharda1 个月前

Collision mesh alongside the splat is the practical bit. Sim geometry is only useful if the onboard perception stack sees the same world at runtime, and that's where the gap usually opens: depth from a real camera on an Orin is noisier and slower than the twin implies. We spend most of our PipeGen work at CraftifAI closing exactly that sim-to-device gap.

Alexi Derkatsch 的头像

Alexi Derkatsch1 个月前

interesting

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49,566 次观看 • 4 个月前

Another city. Another full 3D Gaussian Splat reconstruction. This entire neighborhood was captured while walking with an Insta360 X5, then processed with our GPU pipeline and transformed into a high-fidelity, machine-readable 3D environment with Over the Reality. What you’re seeing is not an isolated virtual world. It’s a 3D digital twin of a real-world location, built from structured spatial data where every building, street, and detail can become usable by AI, robotics, and XR. And it’s happening at scale, across cities in more than 180 countries every day. Have an Insta360 X5 and want to become a mapper? Apply here: Want to build a 3D Gaussian Splat from a video you already recorded? Do it for free at powered by Over the Reality.

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CyberRobo

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16,816 次观看 • 2 个月前

Your best Gaussian Splat might already be sitting in your Scaniverse account. The #SplatYourWorld Challenge is open through July 31. Submit your best capture for a chance to win a share of $4,000 USD in prizes. We're looking for standout 3D reconstructions of: 🏭 Industrial environments 🌲 Natural landscapes 🏙️ Urban infrastructure No need to create something new—just submit your best Gaussian Splat and put the prize money toward new gear, more captures, or whatever powers your workflow. 📅 Deadline: July 31, 11:59 PM PT Learn more: #NianticSpatial #3DReconstruction #DigitalTwins #PhysicalAI #GeospatialAI #AI #3DGS

Your best Gaussian Splat might already be sitting in your Scaniverse account. The #SplatYourWorld Challenge is open through July 31. Submit your best capture for a chance to win a share of $4,000 USD in prizes. We're looking for standout 3D reconstructions of: 🏭 Industrial environments 🌲 Natural landscapes 🏙️ Urban infrastructure No need to create something new—just submit your best Gaussian Splat and put the prize money toward new gear, more captures, or whatever powers your workflow. 📅 Deadline: July 31, 11:59 PM PT Learn more: #NianticSpatial #3DReconstruction #DigitalTwins #PhysicalAI #GeospatialAI #AI #3DGS

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27,130 次观看 • 2 个月前

Trained a humanoid entirely in a 3D scan of the office. Zero real-world fine-tuning. It just walked in and worked. RL needs hundreds of thousands of attempts, and real robots can't afford to crash. A misjudged gap or a glass door collision breaks hardware and costs hours resetting. So you train in a sim. But sim policies usually train on randomized, untextured geometry; depth is easy to fake. The robot learns structure, not the real world: no materials, no lighting, no idea what anything actually is. RGB cameras carry all of that but training RGB policies in generic fake worlds won’t generalize to the real world. Niantic Spatial 🌎 Scaniverse reconstructs your scan of the real deployment site. One 360° camera walkthrough → photorealistic 3D Gaussian splat at metric scale → collision mesh pulled from the same reconstruction, so vision and physics match exactly. Drops straight into NVIDIA Isaac Sim/Lab, no manual conversion. Flexion simulation-first approach then seamlessly enables the training of RGB-only nav policies inside that reconstruction. With added domain randomization + large image encoders for robustness, this deploys straight to hardware. No real-world fine-tuning. Deployment: months of on-site adaptation → days. Tune into the NVIDIA livestream on 12 August to hear how these companies are closing the sim2real gap: NVIDIA Robotics ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Trained a humanoid entirely in a 3D scan of the office. Zero real-world fine-tuning. It just walked in and worked. RL needs hundreds of thousands of attempts, and real robots can't afford to crash. A misjudged gap or a glass door collision breaks hardware and costs hours resetting. So you train in a sim. But sim policies usually train on randomized, untextured geometry; depth is easy to fake. The robot learns structure, not the real world: no materials, no lighting, no idea what anything actually is. RGB cameras carry all of that but training RGB policies in generic fake worlds won’t generalize to the real world. Niantic Spatial 🌎 Scaniverse reconstructs your scan of the real deployment site. One 360° camera walkthrough → photorealistic 3D Gaussian splat at metric scale → collision mesh pulled from the same reconstruction, so vision and physics match exactly. Drops straight into NVIDIA Isaac Sim/Lab, no manual conversion. Flexion simulation-first approach then seamlessly enables the training of RGB-only nav policies inside that reconstruction. With added domain randomization + large image encoders for robustness, this deploys straight to hardware. No real-world fine-tuning. Deployment: months of on-site adaptation → days. Tune into the NVIDIA livestream on 12 August to hear how these companies are closing the sim2real gap: NVIDIA Robotics ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

119,739 次观看 • 1 个月前

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

Flexion

72,964 次观看 • 2 个月前

Since 1898, the San Francisco Ferry Building along the Embarcadero has been a gateway to the city, once bustling with ferry commuters as a primary travel hub. Today, it’s a lively marketplace where local artisans, food vendors, and a popular farmers market bring history and community to life. Using Scaniverse, Niantic Spatial captured the Ferry Building and surroundings in 3D with Gaussian splats—turning a mobile scan into an immersive, navigable digital twin. This opens up new possibilities: 🌍 urban planning, 🎟️ cultural storytelling, 🤖 robotics, and 🛍️ interactive retail & events. Learn more: #NianticSpatial #Scaniverse #3D #Gaussiansplat #Digitaltwins

Since 1898, the San Francisco Ferry Building along the Embarcadero has been a gateway to the city, once bustling with ferry commuters as a primary travel hub. Today, it’s a lively marketplace where local artisans, food vendors, and a popular farmers market bring history and community to life. Using Scaniverse, Niantic Spatial captured the Ferry Building and surroundings in 3D with Gaussian splats—turning a mobile scan into an immersive, navigable digital twin. This opens up new possibilities: 🌍 urban planning, 🎟️ cultural storytelling, 🤖 robotics, and 🛍️ interactive retail & events. Learn more: #NianticSpatial #Scaniverse #3D #Gaussiansplat #Digitaltwins

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12,007 次观看 • 6 个月前

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Have you ever seen a robot walk along a waterfront, understand its surroundings, and look out at the sea? 🤖 Thanks to Over the Reality, this is possible. Places like this are mapped every day by our global community of 360 mappers. Using our technology, they are transformed into high-fidelity machine-readable 3D Gaussian Splats. This data can be used to: • navigate robots through the physical world • train AI and Visual Models • power VPS localization • anchor persistent XR content to real-world locations This is just one of more than 250,000 real-world locations already mapped by the Over the Reality community. And unlike traditional datasets, these places can be explored directly through a smartphone, browser, or XR headset, allowing anyone to navigate and experience them in first person. From city streets and waterfronts to landmarks and cultural sites, we're building one of the world's largest machine-readable maps for AI, robotics, and spatial computing.

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32,061 次观看 • 4 个月前

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The first global, city-scale 3D map is taking shape, and it’s machine-readable. City-scale environments can now be reconstructed in high-fidelity, reaching a level of detail that is becoming almost indistinguishable from reality. This leap is made possible by large-scale mapping using just an Insta360 X5 and Over the Reality technology. So far, our dataset includes: - 220,000+ 3D mapped locations - 97M images - 1,000TB of spatial data Growing by more than 10,000+ newly mapped locations every week. Enabling Visual AI, Robotics, VPS, XR, and Digital twins.

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69,924 次观看 • 5 个月前

3d visual positioning experiment -- look at the alignment between the 3d mesh and the live camera view. Truly feels magical -- like x-ray vision. Workflow: 1. Scanned a street in 15 mins w/ xgrids 2. Localized against that scan at night, in real-time, while sitting in a car The xgrids scanner (w/ rgb + lidar) is perfect to build maps for humans (3d gaussian splats). Then multiset ai makes it easy to build machine-readable maps for AR and robotics to figure out exactly where they are in 3d space with cm-level accuracy.

3d visual positioning experiment -- look at the alignment between the 3d mesh and the live camera view. Truly feels magical -- like x-ray vision. Workflow: 1. Scanned a street in 15 mins w/ xgrids 2. Localized against that scan at night, in real-time, while sitting in a car The xgrids scanner (w/ rgb + lidar) is perfect to build maps for humans (3d gaussian splats). Then multiset ai makes it easy to build machine-readable maps for AR and robotics to figure out exactly where they are in 3d space with cm-level accuracy.

Bilawal Sidhu

27,514 次观看 • 10 个月前

AirVis Studio: Lets you convert Images / Video (Including 360 video) to Gaussian Splat scene locally in your machine for free. Now Available in Windows, works with Intel, AMD, NVIDIA. Also available in Mac with Apple Silicon. No sign up, No subscription, No limits. Enjoy !!! Technical Details: - Metal / Vulkan accelerated SFM (Colmap fork) and Splat Trainer for maximum speed and compatibility. - A 5 Min 360 video took about 2 hours in a modern PC with dedicated GPU. - Ships with SOTA Splat rendering engine : Auto LOD and Collision generation, tested on scenes with more than 250 million splats. And yes, No LOD popping. - Render and export videos with keyframes. - One click publish to AirVis and share with others. Also view in VR, standalone or CloudXR with Foveated Rendering for VisionPro. - And many more, check it out.

AirVis Studio: Lets you convert Images / Video (Including 360 video) to Gaussian Splat scene locally in your machine for free. Now Available in Windows, works with Intel, AMD, NVIDIA. Also available in Mac with Apple Silicon. No sign up, No subscription, No limits. Enjoy !!! Technical Details: - Metal / Vulkan accelerated SFM (Colmap fork) and Splat Trainer for maximum speed and compatibility. - A 5 Min 360 video took about 2 hours in a modern PC with dedicated GPU. - Ships with SOTA Splat rendering engine : Auto LOD and Collision generation, tested on scenes with more than 250 million splats. And yes, No LOD popping. - Render and export videos with keyframes. - One click publish to AirVis and share with others. Also view in VR, standalone or CloudXR with Foveated Rendering for VisionPro. - And many more, check it out.

Arun Kurian

51,041 次观看 • 1 个月前