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Gave a robot 3D vision with just a regular camera👁️ Full Tutorial: Deployed #Depth Anything V3​ on NVIDIA Robotics Jetson AGX Orin. It estimates depth from 2D images in real-time—no special sensors needed. Just need #Monocular depth estimation + #TensorRT​ optimization + #ROS2​ integration. 👉 Learn more about reComputer...

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Seeed Studio

72,599 subscribers

16,966 次观看 • 3 个月前 •via X (Twitter)

#Depth#Monocular
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Spent some time this week playing with depth estimation models. Monocular depth estimation has come a long way, but in terms of metric accuracy and geometric consistency, stereo depth still has it beat by a wide margin. DepthPro (mono) vs FoundationStereo (stereo)
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Spent some time this week playing with depth estimation models. Monocular depth estimation has come a long way, but in terms of metric accuracy and geometric consistency, stereo depth still has it beat by a wide margin. DepthPro (mono) vs FoundationStereo (stereo)

FrostyFridge

27,774 次观看 • 1 年前

Meet Seeed at #GTC2026 in Day1! We're live with: ▶️ #reBot B601 Arm: Watch it doing real-time teleoperation + NVIDIA Robotics Isaac Sim visualization. ▶️ #Openclaw+#JetsonThor→Robot Control: Jetson Thor running local #LLMs to control a robot with natural language. ▶️ #ReachyMini: powered by the #reComputer J4012 with #JetsonOrinNX, reading your vibe with on-device vision & AI. Touch, test, and talk tech with us! 📍San Jose, California, USA 📅 March 16th–19th 🏛️ San Jose Convention Centee, Booth 156 #theAIHardwarePartner
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Meet Seeed at #GTC2026 in Day1! We're live with: ▶️ #reBot B601 Arm: Watch it doing real-time teleoperation + NVIDIA Robotics Isaac Sim visualization. ▶️ #Openclaw+#JetsonThor→Robot Control: Jetson Thor running local #LLMs to control a robot with natural language. ▶️ #ReachyMini: powered by the #reComputer J4012 with #JetsonOrinNX, reading your vibe with on-device vision & AI. Touch, test, and talk tech with us! 📍San Jose, California, USA 📅 March 16th–19th 🏛️ San Jose Convention Centee, Booth 156 #theAIHardwarePartner

Seeed Studio

13,558 次观看 • 2 个月前

AnyDepth: Lightweight zero-shot monocular depth estimation; surpasses DPT; - nicely preserves detail.
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AnyDepth: Lightweight zero-shot monocular depth estimation; surpasses DPT; - nicely preserves detail.

Wildminder

18,152 次观看 • 4 个月前

Depth Anything V2 is a total beast for real-time 3D. Built a website that hits 30fps+ depth reconstructions from a single camera feed, extruding 12k voxels via WebGPU. Pure ML running local Vision Transformers with Three.js and zero backend lag. Lmk in the comments if you want a copy! 🙌 Experience and music made with AI in Omma (tool by Spline).
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Depth Anything V2 is a total beast for real-time 3D. Built a website that hits 30fps+ depth reconstructions from a single camera feed, extruding 12k voxels via WebGPU. Pure ML running local Vision Transformers with Three.js and zero backend lag. Lmk in the comments if you want a copy! 🙌 Experience and music made with AI in Omma (tool by Spline).

Gábor Pribék

141,088 次观看 • 1 个月前

One more turn with this #AR: added underwater transition. All real-time, based on #ML scene depth estimation. Built in Effect House.
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One more turn with this #AR: added underwater transition. All real-time, based on #ML scene depth estimation. Built in Effect House.

Denis Rossiev ᯅ/acc

23,561 次观看 • 2 年前

Video diffusion models are just overqualified depth estimators! Deterministic single-pass depth estimation based on WanV2.1. - SOTA 5.5 AbsRel on ScanNet - data-efficient than baselines; - no temporal flicker + infinite-length estimation w/ zero scale drift.
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Video diffusion models are just overqualified depth estimators! Deterministic single-pass depth estimation based on WanV2.1. - SOTA 5.5 AbsRel on ScanNet - data-efficient than baselines; - no temporal flicker + infinite-length estimation w/ zero scale drift.

Wildminder

49,209 次观看 • 2 个月前

Turning mobile photos into robot-ready 3D worlds just got easier. 🤖 With NVIDIA Omniverse NuRec, developers can reconstruct lifelike environments for robotics simulation using a smartphone. Learn how ➡️
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Turning mobile photos into robot-ready 3D worlds just got easier. 🤖 With NVIDIA Omniverse NuRec, developers can reconstruct lifelike environments for robotics simulation using a smartphone. Learn how ➡️

NVIDIA Omniverse

20,122 次观看 • 7 个月前

Discover the right 3D Geometric Foundation Model for your task—whether it’s stereo matching, multi-view depth estimation, video depth, pose estimation, semantic understanding, or novel view synthesis. Explore more insights in our #E3DBench #FoundationModel #3D #GaussianSplatting. Project Webpage:
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Discover the right 3D Geometric Foundation Model for your task—whether it’s stereo matching, multi-view depth estimation, video depth, pose estimation, semantic understanding, or novel view synthesis. Explore more insights in our #E3DBench #FoundationModel #3D #GaussianSplatting. Project Webpage:

Zhiwen(Aaron) Fan

29,920 次观看 • 1 年前

Google just revealed an ABSOLUTE depth estimation model 🤯 As opposed to recent depth models (Marigold, PatchFusion) which aim for maximum details, DMD aims to estimate the ABSOLUTE depth (in meters) within the image More details below ⬇️⬇️
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Google just revealed an ABSOLUTE depth estimation model 🤯 As opposed to recent depth models (Marigold, PatchFusion) which aim for maximum details, DMD aims to estimate the ABSOLUTE depth (in meters) within the image More details below ⬇️⬇️

Alex Carlier

199,514 次观看 • 2 年前

improving my 3D hand tracking template by adding depth-anything-v2 using mediapipe for X/Y movement and depth-anything for Z movement here i'm controlling a threejs scene in realtime with input from a single webcam
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improving my 3D hand tracking template by adding depth-anything-v2 using mediapipe for X/Y movement and depth-anything for Z movement here i'm controlling a threejs scene in realtime with input from a single webcam

AA

28,068 次观看 • 1 个月前

I added a node to my custom node that converts the depth image to a normal map! This is useful for 2D-style images where Normals Estimation doesn't work well.
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I added a node to my custom node that converts the depth image to a normal map! This is useful for 2D-style images where Normals Estimation doesn't work well.

toyxyz

117,354 次观看 • 1 年前

Happy Monday! We just added raycast sensors to mjlab. They let a robot sample the scene with virtual rays, returning distances and surface geometry. Rays can use orthographic projection for terrain scanning or perspective projection for simulating a depth camera.
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Happy Monday! We just added raycast sensors to mjlab. They let a robot sample the scene with virtual rays, returning distances and surface geometry. Rays can use orthographic projection for terrain scanning or perspective projection for simulating a depth camera.

Kevin Zakka

19,870 次观看 • 5 个月前

Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:
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Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:

Ilir Aliu

53,749 次观看 • 2 个月前

Left: raw depth from the OAK-D Pro W camera Right: depth from FoundationStereo
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Left: raw depth from the OAK-D Pro W camera Right: depth from FoundationStereo

Yu Xiang

32,598 次观看 • 3 个月前

From simulation to reality 🤖 Robotics creator Skyentific built a walking bipedal robot using a simulation-first approach to design, test, and iterating in virtual environments before deploying in the real world. Powered by the NVIDIA Isaac platform and NVIDIA Jetson for on-device AI and control. 📖 #NationalRoboticsWeek
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From simulation to reality 🤖 Robotics creator Skyentific built a walking bipedal robot using a simulation-first approach to design, test, and iterating in virtual environments before deploying in the real world. Powered by the NVIDIA Isaac platform and NVIDIA Jetson for on-device AI and control. 📖 #NationalRoboticsWeek

NVIDIA Robotics

21,584 次观看 • 1 个月前

🤔Applying Depth Estimation models directly to videos can result in inconsistency between frames. 💪Well, not anymore. 🔥ChronoDepth is a new approach to video depth estimation that focuses on achieving both accuracy within each frame and consistency across frames. 📜More👇
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🤔Applying Depth Estimation models directly to videos can result in inconsistency between frames. 💪Well, not anymore. 🔥ChronoDepth is a new approach to video depth estimation that focuses on achieving both accuracy within each frame and consistency across frames. 📜More👇

Gradio

19,986 次观看 • 2 年前

Introducing 🛹 RollingDepth 🛹 — a universal monocular depth estimator for arbitrarily long videos! Our paper, “Video Depth without Video Models,” delivers exactly that, setting new standards in temporal consistency. Check out more details in the thread 🧵
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Introducing 🛹 RollingDepth 🛹 — a universal monocular depth estimator for arbitrarily long videos! Our paper, “Video Depth without Video Models,” delivers exactly that, setting new standards in temporal consistency. Check out more details in the thread 🧵

Anton Obukhov

49,820 次观看 • 1 年前

Revisiting this game a few months later: (i) It's pretty impressive that general-purpose image generators can estimate meaningful depth from a single photo. (ii) Monocular depth remains pretty awful for 3D reconstruction! Here Gemini > GPT > Qwen. (At least I hope so!…🫣)
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Revisiting this game a few months later: (i) It's pretty impressive that general-purpose image generators can estimate meaningful depth from a single photo. (ii) Monocular depth remains pretty awful for 3D reconstruction! Here Gemini > GPT > Qwen. (At least I hope so!…🫣)

Keenan Crane

33,746 次观看 • 1 个月前

BaBot: A Ball-Balancing Robotics Kit BaBot is a do-it-yourself ball-balancing robot kit designed as a hands-on platform for learning robotics and control systems. It enables users to explore concepts such as PID control, sensors, actuators, and software integration through practical experimentation. The kit is intended for students, makers, and others interested in applied robotics without requiring a formal engineering background. Credit: Babot Project #arduino #ai #robotics #3dprint #engineering
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BaBot: A Ball-Balancing Robotics Kit BaBot is a do-it-yourself ball-balancing robot kit designed as a hands-on platform for learning robotics and control systems. It enables users to explore concepts such as PID control, sensors, actuators, and software integration through practical experimentation. The kit is intended for students, makers, and others interested in applied robotics without requiring a formal engineering background. Credit: Babot Project #arduino #ai #robotics #3dprint #engineering

Wevolver

25,314 次观看 • 4 个月前