正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Introducing Depth Estimation with NVIDIA’s FoundationStereo—a new open tutorial and codebase for metric depth sensing using Project Aria Gen 2 devices. ⚡️ Real-time ready (20 FPS) 🤖 Zero-shot generalization ⚡️Stereo image rectification and disparity estimation 🔓 Open Source Check out the code and tutorial: 🔌Chris Sweeney

Project Aria @Meta

1,878 subscribers

32,846 次观看 • 16 天前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

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)

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

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

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

Wildminder

18,152 次观看 • 5 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:

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

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 Robotics J5011: #TheAIHardwarePartner

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 Robotics J5011: #TheAIHardwarePartner

Seeed Studio

16,966 次观看 • 3 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👇

🤔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

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.

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

49,251 次观看 • 3 个月前

Want to use tactile sensing but not familiar with hardware? No worries! Just follow the steps, and you’ll have a high-resolution tactile sensor ready in 30 mins! It’s as simple as making a sandwich! 🥪 🎥 YouTube Tutorial: 🛠️ Open Source & Hardware Guide: 🌐 Project Website: Let’s make robotics more tactile! 🤖 #Robotics #TactileSensing #AI #opensource

Want to use tactile sensing but not familiar with hardware? No worries! Just follow the steps, and you’ll have a high-resolution tactile sensor ready in 30 mins! It’s as simple as making a sandwich! 🥪 🎥 YouTube Tutorial: 🛠️ Open Source & Hardware Guide: 🌐 Project Website: Let’s make robotics more tactile! 🤖 #Robotics #TactileSensing #AI #opensource

Binghao Huang

31,584 次观看 • 1 年前

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

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

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 ⬇️⬇️

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

199,524 次观看 • 2 年前

Current status: In love with the new Angular tutorial! Just open a page, code and learn. No setup. No stress. Just play. 🥹

Current status: In love with the new Angular tutorial! Just open a page, code and learn. No setup. No stress. Just play. 🥹

bolt.new

49,272 次观看 • 2 年前

Open UI Local 1 Click Launcher Open UI from wan deeee bee is a cool AI app that live-generates UIs. It's like Vercel's V0 but open source, and works with both ChatGPT & open source models via ollama Make sure to check out Matthew Berman's tutorial, and run it with 1 click.

Open UI Local 1 Click Launcher Open UI from wan deeee bee is a cool AI app that live-generates UIs. It's like Vercel's V0 but open source, and works with both ChatGPT & open source models via ollama Make sure to check out Matthew Berman's tutorial, and run it with 1 click.

cocktail peanut

53,965 次观看 • 2 年前

Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:

Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:

PyCharm, a JetBrains IDE

34,908 次观看 • 1 个月前

An image + a detailed normal map generated by GeoWizard --> a relightable image. Geowizard is a generative geometry estimation model that could jointly produce high-quality depth and normal with intricate details. Try our huggingface demo here: #huggingface #gradio #geowizard

An image + a detailed normal map generated by GeoWizard --> a relightable image. Geowizard is a generative geometry estimation model that could jointly produce high-quality depth and normal with intricate details. Try our huggingface demo here: #huggingface #gradio #geowizard

Xiao-Xiao Long

30,338 次观看 • 2 年前

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

MrNeRF

27,428 次观看 • 1 年前

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.

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

Skywork Matrix-Game 3.0 is here! FULLY OPEN SOURCE! Real-Time and Streaming Interactive World Model with Long-Horizon Memory - Fully open source: code, model, and technical report - 720p @ 40FPS with a 5B model - Minute-long memory consistency - Trained on Unreal Engine + AAA games + real-world data - Scales up to 28B MoE for quality, dynamics, and generalization Homepage 👉 Code 👉 Model 👉 Tech report 👉 Create. Explore. Play. With Matrix-Game 3.0

Skywork Matrix-Game 3.0 is here! FULLY OPEN SOURCE! Real-Time and Streaming Interactive World Model with Long-Horizon Memory - Fully open source: code, model, and technical report - 720p @ 40FPS with a 5B model - Minute-long memory consistency - Trained on Unreal Engine + AAA games + real-world data - Scales up to 28B MoE for quality, dynamics, and generalization Homepage 👉 Code 👉 Model 👉 Tech report 👉 Create. Explore. Play. With Matrix-Game 3.0

Skywork

54,545 次观看 • 2 个月前

Introducing Nymeria, a dataset for improving egocentric human motion understanding for AR and VR devices from Project Aria @Meta

Introducing Nymeria, a dataset for improving egocentric human motion understanding for AR and VR devices from Project Aria @Meta

Reality Labs at Meta

14,576 次观看 • 1 年前

🔥New tutorial!🥳 Animate faster in #Blender using pose libraries and the asset browser. Check out the full tutorial on my #Youtube channel. 👉 #p2design #animation #B3D #3D

🔥New tutorial!🥳 Animate faster in #Blender using pose libraries and the asset browser. Check out the full tutorial on my #Youtube channel. 👉 #p2design #animation #B3D #3D

Pierrick Picaut

47,333 次观看 • 11 个月前

Introducing Precedent – building blocks for your @Nextjs project. It's a collection of components, hooks, and utilities that I find myself reusing every time I start a new project. 100% free & open source →

Introducing Precedent – building blocks for your @Nextjs project. It's a collection of components, hooks, and utilities that I find myself reusing every time I start a new project. 100% free & open source →

Steven Tey

480,660 次观看 • 3 年前

Working on a new open source template/demo for Godot. We'll also make a tutorial for it. Character model by Tibo (heytibo.bsky.social) and design by 钛山 (Tyson Tan)

Working on a new open source template/demo for Godot. We'll also make a tutorial for it. Character model by Tibo (heytibo.bsky.social) and design by 钛山 (Tyson Tan)

GDQuest

30,625 次观看 • 1 年前