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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 🌎

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54,070 Aufrufe • vor 1 Monat •via X (Twitter)

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7 Kommentare

Profilbild von ResinBody

ResinBodyvor 1 Monat

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.

Profilbild von arpu

arpuvor 1 Monat

is it opensource?

Profilbild von worksrecorded.com - AI news, construction

worksrecorded.com - AI news, constructionvor 1 Monat

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.

Profilbild von Ian Smith

Ian Smithvor 1 Monat

👀

Profilbild von Geωrge

Geωrgevor 1 Monat

I love this technology... It's amazing

Profilbild von Pratik Sharda

Pratik Shardavor 1 Monat

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.

Profilbild von Alexi Derkatsch

Alexi Derkatschvor 1 Monat

interesting

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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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119,739 Aufrufe • vor 1 Monat

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12,007 Aufrufe • vor 6 Monaten

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27,514 Aufrufe • vor 10 Monaten

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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 Aufrufe • vor 1 Monat