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Construction sites change fast. Constant site revisits are expensive and time-consuming. With Scaniverse, teams can capture and operationalize real-world environments with precision at scale using high-fidelity textured meshes and Gaussian splats. Monitor progress remotely, improve collaboration, document site conditions, and reduce unnecessary visits — all from a phone or... show more

Niantic Spatial 🌎

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16,635 Aufrufe • vor 4 Monaten •via X (Twitter)

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Our product team walks through the full journey: Capture → Reconstruct → Localize Scaniverse creates high-fidelity 3D inputs from the real world—built for reconstruction, localization, and semantic understanding. Watch the full YouTube playlist: #NianticSpatial #Scaniverse #Reconstruction #VPS #GeospatialAI #AI #PhysicalAI

Our product team walks through the full journey: Capture → Reconstruct → Localize Scaniverse creates high-fidelity 3D inputs from the real world—built for reconstruction, localization, and semantic understanding. Watch the full YouTube playlist: #NianticSpatial #Scaniverse #Reconstruction #VPS #GeospatialAI #AI #PhysicalAI

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🌎 The world in your pocket, now in 3D. For a limited time, upgrade to a Scaniverse Annual Plan by June 30 and get: ✅12 months for the price of 10 + up to 105,000 bonus cloud processing credits. ⏰Upgrade today! #NianticSpatial #Scaniverse #Reconstruction #3DScanning #GaussianSplat #360Capture #DigitalTwin #AI #GeospatialAI #PhysicalAI #VPS

🌎 The world in your pocket, now in 3D. For a limited time, upgrade to a Scaniverse Annual Plan by June 30 and get: ✅12 months for the price of 10 + up to 105,000 bonus cloud processing credits. ⏰Upgrade today! #NianticSpatial #Scaniverse #Reconstruction #3DScanning #GaussianSplat #360Capture #DigitalTwin #AI #GeospatialAI #PhysicalAI #VPS

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How do you go from scanning a real-world space → using it in an app, workflow, or environment? Our product team walks through Scaniverse step-by-step, from capturing spaces to generating production-ready 3D assets and enabling precise, real-world positioning. In the first video, Senior Product Manager, Bec Maxworth, walks through your entry point into Scaniverse from signing up to getting started so you can start building high-fidelity 3D models in minutes. Dive into the full walkthrough here: #NianticSpatial #Scaniverse #Reconstruction #VPS #GeospatialAI #AI #PhysicalAI

How do you go from scanning a real-world space → using it in an app, workflow, or environment? Our product team walks through Scaniverse step-by-step, from capturing spaces to generating production-ready 3D assets and enabling precise, real-world positioning. In the first video, Senior Product Manager, Bec Maxworth, walks through your entry point into Scaniverse from signing up to getting started so you can start building high-fidelity 3D models in minutes. Dive into the full walkthrough here: #NianticSpatial #Scaniverse #Reconstruction #VPS #GeospatialAI #AI #PhysicalAI

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Today, Niantic Spatial introduces Scaniverse — our flagship product and the gateway to our spatial intelligence platform and Large Geospatial Model. Here’s what’s new: 🔹Scaniverse (iOS mobile + web): Capture once, generate multiple outputs—VPS maps, meshes, and Gaussian splats (Android coming soon) 🔹Collaborative mapping: Multi-user scans fused into a single, continuously improving model 🔹 On-device validation: Preview VPS coverage and test localization in real time 🔹VPS 2.0: Global-scale positioning—centimeter-accurate where mapped, reliable everywhere else (even where GPS fails) 🔹NSDK 4.0 (coming soon): A unified SDK across Unity, Swift, Android, and ROS 2 Read more: #NianticSpatial #Scaniverse #VPS #GeospatialAI #AI

Today, Niantic Spatial introduces Scaniverse — our flagship product and the gateway to our spatial intelligence platform and Large Geospatial Model. Here’s what’s new: 🔹Scaniverse (iOS mobile + web): Capture once, generate multiple outputs—VPS maps, meshes, and Gaussian splats (Android coming soon) 🔹Collaborative mapping: Multi-user scans fused into a single, continuously improving model 🔹 On-device validation: Preview VPS coverage and test localization in real time 🔹VPS 2.0: Global-scale positioning—centimeter-accurate where mapped, reliable everywhere else (even where GPS fails) 🔹NSDK 4.0 (coming soon): A unified SDK across Unity, Swift, Android, and ROS 2 Read more: #NianticSpatial #Scaniverse #VPS #GeospatialAI #AI

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📲💻Capture and scan with Scaniverse, then bring your sites directly into NSDK 4.0. A unified spatial data pipeline connects capture → reconstruction → localization → runtime—so your scans are immediately usable in your XR projects. Build with Unity, Swift, Kotlin, and early ROS2 support, and deploy high-performance experiences across mobile and robotic systems within your existing workflows. 🔗Get started today: #NianticSpatial #Scaniverse #NDSK #GeospatialAI #PhysicalAI #AI #XR #VR #Robotics #Unity #Kotlin #Swift

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Large-scale Gaussian splats have reached a new level of realism. This is a well-known temple in Bangkok, reconstructed as a high-fidelity 3D environment from 360 captures. At this level, the boundary between video and 3D starts to disappear. But what you’re looking at is not a video. It’s a dense spatial representation of a real place, where geometry, texture, and structure are preserved and made machine-readable. This kind of 3D data can power Visual AI, Robotics navigation, VPS localization, XR experiences, world models, and next-generation spatial computing systems. Built with Over the Reality.

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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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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.

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.

Over the Reality 🌐

400,282 Aufrufe • vor 4 Monaten

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 →

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4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos Abstract: We propose 4DGT, a 4D Gaussian-based Transformer model for dynamic scene reconstruction, trained entirely on real-world monocular posed videos. Using 4D Gaussian as an inductive bias, 4DGT unifies static and dynamic components, enabling the modeling of complex, time-varying environments with varying object lifespans. We introduced a novel density control strategy in training, which allows our 4DGT to handle longer space-time input while maintaining efficient rendering at runtime. Our model processes 64 consecutive posed frames in a rolling-window fashion, predicting consistent 4D Gaussians in the scene. Unlike optimization-based methods, 4DGT performs purely feed-forward inference, reducing reconstruction time from hours to seconds and scaling effectively to long video sequences. Trained only on large-scale monocular posed video datasets, 4DGT can significantly outperform prior Gaussian-based networks in real-world videos and achieve on-par accuracy with optimization-based methods on cross-domain videos.

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Mansa AI is an enterprise-grade AI + Web3 platform designed to move artificial intelligence from experimentation into real-world execution. Built for creators, developers, and businesses, it focuses on deploying AI that actually works across modern digital systems, not just in isolated demos. 🚀 Production-ready AI infrastructure Mansa AI enables teams to deploy AI systems designed for live environments, handling real workflows, real data, and real operational demands without constant manual oversight. 🧠 Autonomous AI agents At its core, Mansa AI allows users to build autonomous agents that automate decision-making, coordinate tasks, monitor live signals, and execute complex workflows across dynamic environments. ⚙️ Fully customizable logic Agents can be configured with custom behaviors, triggers, and responses. From content generation and analytics to operational automation and intelligent orchestration, logic adapts to specific business strategies. 🔗 Web3 and off-chain integration Mansa AI bridges blockchain ecosystems with traditional systems, enabling cross-chain coordination, smart contract interactions, and seamless integration with existing enterprise infrastructure. 📊 Real-world use cases The platform supports automation for operations, customer engagement, analytics, data pipelines, content workflows, and AI-driven optimization across products and teams. 📈 Built for scale Whether launching as a startup or deploying across enterprise systems, Mansa AI is designed to scale AI operations without adding complexity or fragmentation. Mansa AI transforms artificial intelligence into deployable infrastructure. By combining autonomy, customization, interoperability, and scalability, it enables teams to own, operate, and grow intelligent systems that deliver real value in production environments.

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Gaussian Splats are quickly becoming one of my favorite ways to move complex 3D assets between workflows. For this test I started with Panthera Spelaea, the Blender 5.2 splash artwork by Joanna Kobierska, available from under CC-BY. Using GPT-6 Astra, I had it build a 200-frame camera rig and send the renders directly to The Render Network through the API and export a COLMAP data set. NEXT: COLMAP → Postshot → Gaussian Splat. The result is a real-time, high-fidelity version of an insanely dense asset - millions of hairs and all - that I can now bring back into OTOY Octane, relight, reframe, drop into other scenes, or even publish straight to the web. Capture the visual complexity once, then carry it anywhere. Check out the splat:

Gaussian Splats are quickly becoming one of my favorite ways to move complex 3D assets between workflows. For this test I started with Panthera Spelaea, the Blender 5.2 splash artwork by Joanna Kobierska, available from under CC-BY. Using GPT-6 Astra, I had it build a 200-frame camera rig and send the renders directly to The Render Network through the API and export a COLMAP data set. NEXT: COLMAP → Postshot → Gaussian Splat. The result is a real-time, high-fidelity version of an insanely dense asset - millions of hairs and all - that I can now bring back into OTOY Octane, relight, reframe, drop into other scenes, or even publish straight to the web. Capture the visual complexity once, then carry it anywhere. Check out the splat:

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11,622 Aufrufe • vor 17 Tagen

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924,166 Aufrufe • vor 7 Monaten

Nvidia announces GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning paper page: Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions, addressing the limitations (e.g., flexibility and efficiency) imposed by mesh or NeRF-based representations. However, a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems, we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animation. Second, to stabilize and amortize the learning of millions of Gaussians, we propose to use neural implicit fields to predict the Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries and extract detailed meshes, we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method, GAvatar, enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality, and achieves extremely fast rendering (100 fps) at 1K resolution.

AK

141,058 Aufrufe • vor 2 Jahren

At about T-8 hours before Starship Flight 13’s third attempt to launch, what are the conditions out at the launch site? What does the weather look like? And, what is it like to be at the launch site and Starbase about 8 or so hours from a launch? Today I talk about all of this and provide some updates (narration & visuals) on the construction around the launch site too & why much of this is necessary. Oh, and a reminder too. If you have ever thought about coming out to Starbase and experience something you can literally not do anywhere else in the world (being at an active rocket launch site with a fully stacked rocket just hours before a launch all up close), then you need to plan on doing so sooner than later. These opportunities may not last forever, and indeed some restrictions continue to be added over time. Good luck to SpaceX and the Starship team … I hope Friday with the 13th launch is lucky indeed! 🚀🍀🤞 Ellie in Space 🚀💫

At about T-8 hours before Starship Flight 13’s third attempt to launch, what are the conditions out at the launch site? What does the weather look like? And, what is it like to be at the launch site and Starbase about 8 or so hours from a launch? Today I talk about all of this and provide some updates (narration & visuals) on the construction around the launch site too & why much of this is necessary. Oh, and a reminder too. If you have ever thought about coming out to Starbase and experience something you can literally not do anywhere else in the world (being at an active rocket launch site with a fully stacked rocket just hours before a launch all up close), then you need to plan on doing so sooner than later. These opportunities may not last forever, and indeed some restrictions continue to be added over time. Good luck to SpaceX and the Starship team … I hope Friday with the 13th launch is lucky indeed! 🚀🍀🤞 Ellie in Space 🚀💫

Joe Tegtmeyer 🚀 🤠🛸😎

28,796 Aufrufe • vor 2 Monaten