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Traditional SLAM: capture lidar, capture camera, fuse in post, correct drift, build map This is a Rev8 OS1 Max running SLAM down Union Street in San Francisco. The colorized point cloud is generated from a single sensor in a single pass, with color and depth captured in the same...

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Ouster

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29,707 просмотров • 2 месяцев назад •via X (Twitter)

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With Rev8, color is perceived in the right place, at the right distance, at the right moment. Ouster L4 silicon captures color and depth in the same photon event, with Fujifilm color science embedded directly on the chip. This is a Rev8 OS1 Max driving the Embarcadero in San Francisco. Every traffic signal, brake light, and bike lane resolved in 48-bit native color, perfectly aligned with depth at the silicon level.
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With Rev8, color is perceived in the right place, at the right distance, at the right moment. Ouster L4 silicon captures color and depth in the same photon event, with Fujifilm color science embedded directly on the chip. This is a Rev8 OS1 Max driving the Embarcadero in San Francisco. Every traffic signal, brake light, and bike lane resolved in 48-bit native color, perfectly aligned with depth at the silicon level.

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This was captured without a camera. Every point carries its true 3D position and 48-bit native color, fused in the same photon event. Here is a palm-lined drive down Beverly Drive in Los Angeles, the trees, the road, and the light all captured with Rev8 lidar alone, accurate from 1 lux to 2 million lux.
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This was captured without a camera. Every point carries its true 3D position and 48-bit native color, fused in the same photon event. Here is a palm-lined drive down Beverly Drive in Los Angeles, the trees, the road, and the light all captured with Rev8 lidar alone, accurate from 1 lux to 2 million lux.

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The team at NVIDIA AI Developer really nailed it with ViPE!! This SLAM system generated pose, depth, point clouds, etc, in a few seconds! Now to get the output to work with GEN3C... If you are interested in running this yourself, it took about 5 minutes to get up and running from scratch.
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The team at NVIDIA AI Developer really nailed it with ViPE!! This SLAM system generated pose, depth, point clouds, etc, in a few seconds! Now to get the output to work with GEN3C... If you are interested in running this yourself, it took about 5 minutes to get up and running from scratch.

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LEGO-SLAM: Language-Embedded Gaussian Optimization SLAM LEGO-SLAM running at 15 FPS on a ScanNet scene with language-based loop closing for drift correction. LEGO-SLAM is a 3DGS-based SLAM framework that supports open-vocabulary semantic querying and rendering. It tracks via G-ICP and efficiently builds a map by embedding Gaussians with scene-adaptive 16D language features. Map management is achieved through Language Pruning and Language-Based Loop Detection. The generated map enables open-vocabulary 3D Object Localization.
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LEGO-SLAM: Language-Embedded Gaussian Optimization SLAM LEGO-SLAM running at 15 FPS on a ScanNet scene with language-based loop closing for drift correction. LEGO-SLAM is a 3DGS-based SLAM framework that supports open-vocabulary semantic querying and rendering. It tracks via G-ICP and efficiently builds a map by embedding Gaussians with scene-adaptive 16D language features. Map management is achieved through Language Pruning and Language-Based Loop Detection. The generated map enables open-vocabulary 3D Object Localization.

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Second career grand slam for Jhonny Level is a moonshot! 🌕 The SFGiants' No. 4 prospect is slashing .444/.490/.889 for the Single-A San Jose Giants, leading the circuit in OPS (1.379):

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Build Showcase is a series where we look at builds from the community. This Titan was put together by DeadRabbit and uses the latest in slam technology to warp around the map, blow up screens and chunk Pinnacle Bosses in seconds.
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Blackmagic Design just made a huge announcement at NAB 2025 — revealing their full immersive video workflow from camera to Apple Vision Pro. In this video, we walk through the Blackmagic URSA Cine Immersive pipeline, from capture in Blackmagic RAW, through DaVinci Resolve, all the way to native immersive playback on Apple Vision Pro using MV-HEVC metadata. We also sat down with Blackmagic Design to discuss the latest URSA Cine Immersive firmware and UIs update, immersive color grading workflows, and a powerful REST API that allows remote camera control — perfect for broadcast and remote Immersive 180 capture environments. Captured live at NAB 2025, this is the first in-depth look at how Blackmagic is setting a new standard for immersive filmmaking, delivering true 8K-per-eye playback with zero baked-in rendering and no double-pass quality loss. Comment “URSA Cine” for the full 30-minute in-depth video. #ursacineimmersive #applevisionpro #visionpro

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I prompted Omma to build a #threejs app with real dynamic lighting on a 3D mesh - generated from a single image, using Depth Anything v2 + Transformers from Hugging Face ⚡ Depth estimation in the browser 🫧 3D mesh from the depth map 💡 Dynamic lighting reacting to the geometry No manual code. Pure AI prompting. The future of 3D on the web feels wide open.
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Full article capture to Notion on iPhone! This is a super early look at Flylighter on iOS. My team got this initial test build of the app up and running in a single month. Absolutely incredible. Next steps: ✅ Speed upgrades ✅ More capture modes - text highlights, voice recordings ✅ Capture history (so you can send a highlight to a previously captured page) Soon, you’ll have a full set of tools for capturing any idea or resource you find to Notion, instantly. Less friction = more action 💪

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In Brazil, street surveillance cameras captured a hilarious moment where a "husband running away from his wife," a "cat running away from a dog," and a "thief running away from the police" all appeared at the same time. 😂

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For the past 3 months, I've been heads down building and extending our multimodal data capture rig for Physical AI training. What you're seeing here is a single synchronized capture from our system — RGB, depth (stereo IR camera) feed; real-time 3D hand reconstruction, and a 23×20 tactile pressure grid from sensor gloves, all aligned and timestamped together. Getting here took a lot of unglamorous iteration. We reworked the pipeline to deliver clean, gap-free depth at scale after identifying early depth frame drops caused by data throughput bottlenecks in our capture box. We built per-channel calibration models that convert raw resistance signals from our flex sensor gloves into accurate, drift-corrected force values — solving the creep behavior that made a steady grip falsely appear to fade over time. Even though we’ve stayed quiet on socials, word of mouth spread — our rigs have already been deployed in data factories, manufacturing lines, and homes across the US, Europe, China, and Southeast Asia by our customers and ourselves. In the next few days, I'll be sharing more about how we're tackling rights-cleared data capture from the hardware level, and our closed-loop data-model flywheel — using model evaluation results to identify gaps and guide what data our customers need. More soon, stay tuned…

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