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AnyDepth Depth Estimation Made Easy

AK

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14,837 次观看 • 9 个月前 •via X (Twitter)

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DVD Deterministic Video Depth Estimation with Generative Priors paper:

DVD Deterministic Video Depth Estimation with Generative Priors paper:

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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

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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:

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

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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.

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.

Wildminder

59,393 次观看 • 9 个月前

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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Check-out 📢DUSt3R📢 - a new 3D reconstruction model that works with no prior info on camera calibration nor viewpoint poses! Outperforms SoA monocular & multiview depth estimation & relative pose estimation. Paper, demo, videos (& soon code!) available

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NAVER LABS Europe

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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

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Anirudha Majumdar

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Presenting MonoNeRF at #ICML2023 We train a generalizable NeRF from: ✅Large-scale monocular videos instead of one scene ✅No GT camera poses.📷🚫 Without per-scene optimization, the model can do view synthesis, depth estimation, camera pose estimation.

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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

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Wally has made roughly $28 million in his career and he’s saved every penny of it, by my estimation.

Wally has made roughly $28 million in his career and he’s saved every penny of it, by my estimation.

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