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InfiniDepth: Fine-grained depth estimation at any resolution; models depth as a neural implicit field instead of a grid; - tops DepthAnything; - recovers sharp details w/ a lightweight 15M decoder.

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Wildminder

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59,172 просмотров • 5 месяцев назад •via X (Twitter)

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Excited to share our work InfiniDepth (CVPR 2026) — casting monocular depth estimation as a neural implicit field, which enables: 🔍 Arbitrary-Resolution 📐 Accurate Metric Depth 📷 Large-View Novel View Synthesis Feel free to try our code:
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Excited to share our work InfiniDepth (CVPR 2026) — casting monocular depth estimation as a neural implicit field, which enables: 🔍 Arbitrary-Resolution 📐 Accurate Metric Depth 📷 Large-View Novel View Synthesis Feel free to try our code:

Sida Peng

38,707 просмотров • 3 месяцев назад

Thrilled to introduce Video Depth Anything to support Depth Estimation for super-long videos (over 5 minutes). 👉It enjoys all the benefits of #DepthAnything: high-quality, fast, robust, etc. Proj Page:
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Thrilled to introduce Video Depth Anything to support Depth Estimation for super-long videos (over 5 minutes). 👉It enjoys all the benefits of #DepthAnything: high-quality, fast, robust, etc. Proj Page:

Bingyi Kang

25,464 просмотров • 1 год назад

TikTok presents Depth Anything Unleashing the Power of Large-Scale Unlabeled Data paper page: demo: Depth Anything is trained on 1.5M labeled images and 62M+ unlabeled images jointly, providing the most capable Monocular Depth Estimation (MDE) foundation models with the following features: zero-shot relative depth estimation, better than MiDaS v3.1 (BEiTL-512) zero-shot metric depth estimation, better than ZoeDepth optimal in-domain fine-tuning and evaluation on NYUv2 and KITTI
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TikTok presents Depth Anything Unleashing the Power of Large-Scale Unlabeled Data paper page: demo: Depth Anything is trained on 1.5M labeled images and 62M+ unlabeled images jointly, providing the most capable Monocular Depth Estimation (MDE) foundation models with the following features: zero-shot relative depth estimation, better than MiDaS v3.1 (BEiTL-512) zero-shot metric depth estimation, better than ZoeDepth optimal in-domain fine-tuning and evaluation on NYUv2 and KITTI

AK

600,018 просмотров • 2 лет назад

📢Panoptic Lifting for 3D Scene Understanding with Neural Fields #CVPR23 highlight! Given only posed RGB images of a scene, we optimize a panoptic radiance field representing color, depth, semantics, and instances at any point in space. Vid: Yawar Siddiqui
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📢Panoptic Lifting for 3D Scene Understanding with Neural Fields #CVPR23 highlight! Given only posed RGB images of a scene, we optimize a panoptic radiance field representing color, depth, semantics, and instances at any point in space. Vid: Yawar Siddiqui

Matthias Niessner

29,830 просмотров • 3 лет назад

How can lightweight drones without depth cameras navigate using monocular images? Check out our paper at ISER 2023! MonoNav: MAV Navigation via Monocular Depth Estimation and Reconstruction arXiv: website: Work led by Nate Simon
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How can lightweight drones without depth cameras navigate using monocular images? Check out our paper at ISER 2023! MonoNav: MAV Navigation via Monocular Depth Estimation and Reconstruction arXiv: website: Work led by Nate Simon

Anirudha Majumdar

27,642 просмотров • 2 лет назад

After a year of team work, we're thrilled to introduce Depth Anything 3 (DA3)! 🚀 Aiming for human-like spatial perception, DA3 extends monocular depth estimation to any-view scenarios, including single images, multi-view images, and video. In pursuit of minimal modeling, DA3 reveals two key insights: 💎 A plain transformer (e.g., vanilla DINO) is enough. No specialized architecture. ✨ A single depth-ray representation is enough. No complex 3D tasks. Three series of models have been released: the main DA3 series, a monocular metric estimation series, and a monocular depth estimation series. The core team members, aside from me: Haotong Lin, Sili Chen, Jun Hao Liew, Donny Y. Chen. 👇(1/n) #DepthAnything3
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After a year of team work, we're thrilled to introduce Depth Anything 3 (DA3)! 🚀 Aiming for human-like spatial perception, DA3 extends monocular depth estimation to any-view scenarios, including single images, multi-view images, and video. In pursuit of minimal modeling, DA3 reveals two key insights: 💎 A plain transformer (e.g., vanilla DINO) is enough. No specialized architecture. ✨ A single depth-ray representation is enough. No complex 3D tasks. Three series of models have been released: the main DA3 series, a monocular metric estimation series, and a monocular depth estimation series. The core team members, aside from me: Haotong Lin, Sili Chen, Jun Hao Liew, Donny Y. Chen. 👇(1/n) #DepthAnything3

Bingyi Kang

514,578 просмотров • 7 месяцев назад

Want to use Depth Anything, but need metric depth rather than relative depth? Thrilled to introduce Prompt Depth Anything, a new paradigm for accurate metric depth estimation with up to 4K resolution. 👉Key Message: Depth foundation models like DA have already internalized rich geometric knowledge of the 3D world but lack a proper way to elicit it. Inspired by the success of prompting in LLMs, we propose prompting Depth Anything with metric cues to produce metric depth. This method proves to be very effective when using a low-cost lidar (e.g., iPhone's LiDAR), which is widely available, as prompts. We believe the prompt can generalize to other forms as long as scale information is provided. Prompt Depth Anything offers 1⃣A series of models for iPhone lidars. 2⃣4D reconstruction from monocular videos (captured with iPhone). 3⃣Improved generalization ability for robot manipulation, e.g. Training on cans but generalizing on glasses. 4⃣More detailed depth annotations for the ScanNet++ dataset. The first author is our excellent intern Haotong Lin. Paper: Huggingface: Project Page: Code:
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Want to use Depth Anything, but need metric depth rather than relative depth? Thrilled to introduce Prompt Depth Anything, a new paradigm for accurate metric depth estimation with up to 4K resolution. 👉Key Message: Depth foundation models like DA have already internalized rich geometric knowledge of the 3D world but lack a proper way to elicit it. Inspired by the success of prompting in LLMs, we propose prompting Depth Anything with metric cues to produce metric depth. This method proves to be very effective when using a low-cost lidar (e.g., iPhone's LiDAR), which is widely available, as prompts. We believe the prompt can generalize to other forms as long as scale information is provided. Prompt Depth Anything offers 1⃣A series of models for iPhone lidars. 2⃣4D reconstruction from monocular videos (captured with iPhone). 3⃣Improved generalization ability for robot manipulation, e.g. Training on cans but generalizing on glasses. 4⃣More detailed depth annotations for the ScanNet++ dataset. The first author is our excellent intern Haotong Lin. Paper: Huggingface: Project Page: Code:

Bingyi Kang

67,604 просмотров • 1 год назад

AnyDepth Depth Estimation Made Easy
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AnyDepth Depth Estimation Made Easy

AK

14,765 просмотров • 5 месяцев назад

Monocular depth estimation is “impossible” because one image can’t measure depth geometrically. Our iDisc #CVPR2023 can group pixels w/o supervision and learn depth inductive bias on groups. We get LiDAR-like (but denser) depth from single images! More:
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Monocular depth estimation is “impossible” because one image can’t measure depth geometrically. Our iDisc #CVPR2023 can group pixels w/o supervision and learn depth inductive bias on groups. We get LiDAR-like (but denser) depth from single images! More:

Fisher Yu

52,267 просмотров • 3 лет назад

Learning Temporally Consistent Video Depth from Video Diffusion Priors This work addresses the challenge of video depth estimation, which expects not only per-frame accuracy but, more importantly, cross-frame consistency. Instead of directly
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Learning Temporally Consistent Video Depth from Video Diffusion Priors This work addresses the challenge of video depth estimation, which expects not only per-frame accuracy but, more importantly, cross-frame consistency. Instead of directly

AK

81,634 просмотров • 2 лет назад

DVD Deterministic Video Depth Estimation with Generative Priors paper:
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DVD Deterministic Video Depth Estimation with Generative Priors paper:

AK

62,042 просмотров • 3 месяцев назад

New camera lens! Playing with depth of field on Father's Day at Citi Field #baseballiscinema
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New camera lens! Playing with depth of field on Father's Day at Citi Field #baseballiscinema

John DeMarsico

204,280 просмотров • 1 год назад

Pixel-Perfect-Depth: the paper aims to fix Marigold's loss of sharpness induced by VAE by using VFMs (VGGT/DAv2) and a DiT-based pixel decoder to refine the predictions and achieve clean depth discontinuities. Video by authors.
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Pixel-Perfect-Depth: the paper aims to fix Marigold's loss of sharpness induced by VAE by using VFMs (VGGT/DAv2) and a DiT-based pixel decoder to refine the predictions and achieve clean depth discontinuities. Video by authors.

Anton Obukhov

29,512 просмотров • 8 месяцев назад

Depth Anything V2 This work presents Depth Anything V2. Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation model. Notably, compared with V1, this version produces much finer and more
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Depth Anything V2 This work presents Depth Anything V2. Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation model. Notably, compared with V1, this version produces much finer and more

AK

114,537 просмотров • 2 лет назад

Implicit Neural Light Spheres lets you turn panoramic captures into dynamic wide FOV renders (with real-time rendering!). Instead of generating panoramas with image stitching, we use neural light spheres to jointly estimate the camera path and a high-resolution scene reconstruction to produce novel wide field-of-view projections of the environment. Code, data, and info: Amazing work with Ilya Chugunov, Amogh Joshi, Kiran Murthy, François Bleibel
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Implicit Neural Light Spheres lets you turn panoramic captures into dynamic wide FOV renders (with real-time rendering!). Instead of generating panoramas with image stitching, we use neural light spheres to jointly estimate the camera path and a high-resolution scene reconstruction to produce novel wide field-of-view projections of the environment. Code, data, and info: Amazing work with Ilya Chugunov, Amogh Joshi, Kiran Murthy, François Bleibel

Felix Heide

10,300 просмотров • 1 год назад

what if painterly depth of field (distracting?)
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what if painterly depth of field (distracting?)

mikoroonii

22,372 просмотров • 9 месяцев назад

Depth-of-Field representation in wireframe space
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Depth-of-Field representation in wireframe space

Yugo Nakamura

243,101 просмотров • 1 год назад

Multi-view Reconstruction via SfM-guided Monocular Depth Estimation Contributions: • We propose a novel approach to inject SfM priors into diffusion-based depth estimation, enabling highly accurate and multi-view consistent depth predictions for each viewpoint. • Based on the proposed depth estimator, we design a new multi-view 3D geometry reconstruction framework and process some synthetic datasets to facilitate training. • We evaluate our method on diverse real-world scene data, including objects, indoor environments, streetscapes, and aerial scenes, demonstrating the superior performance and generalization capability of our approach.
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Multi-view Reconstruction via SfM-guided Monocular Depth Estimation Contributions: • We propose a novel approach to inject SfM priors into diffusion-based depth estimation, enabling highly accurate and multi-view consistent depth predictions for each viewpoint. • Based on the proposed depth estimator, we design a new multi-view 3D geometry reconstruction framework and process some synthetic datasets to facilitate training. • We evaluate our method on diverse real-world scene data, including objects, indoor environments, streetscapes, and aerial scenes, demonstrating the superior performance and generalization capability of our approach.

MrNeRF

25,651 просмотров • 1 год назад

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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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.

Joseph Azar

14,041 просмотров • 2 месяцев назад