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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... show more

MrNeRF

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25,651 просмотров • 1 год назад •via X (Twitter)

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Фото профиля MrNeRF

MrNeRF1 год назад

Method overview!

Фото профиля MrNeRF

MrNeRF1 год назад

Paper: Project: Code:

Фото профиля BSHC

BSHC1 год назад

High-performance wave basins, towing tanks, and cavitation tunnels at competitive prices deliver exceptional value and reliability. Our facilities enable detailed hydrodynamic testing and CFD analysis for accurate evaluation of floating structure designs.

Фото профиля MrNeRF

MrNeRF1 год назад

I'm crafting an email newsletter that turns my daily updates into a captivating weekly digest, complete with exclusive content. Although it's not live yet, you can sign up now! If you're curious, visit my website and join the subscriber list today!

Фото профиля Securade.ai

Securade.ai1 год назад

this is really impressive work on multi-view reconstruction! the use of SfM priors to guide diffusion-based depth estimation seems like a smart way to improve accuracy and consistency. i'll definitely check out the paper and code.

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Meet MapAnything – a transformer that directly regresses factored metric 3D scene geometry (from images, calibration, poses, or depth) in an end-to-end way. No pipelines, no extra stages. Just 3D geometry & cameras, straight from any type of input, delivering new state-of-the-art results 🚀 One universal model enables SoTA for: 🔥 Mono Depth Estimation 🔥 Multi-View SfM 🔥 Multi-View Stereo 🔥 Depth Completion 🔥 Registration … and many more possibilities! – plus everything is metric 🎯 We release code for data processing, training, benchmarking & ablations – everything Apache 2.0! Details & Links 👇

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67,604 просмотров • 1 год назад

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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

[1/N] Current visual geometry prediction models primarily rely on labeled 3D data. Our CVPR26 paper, Flow3r, allows additionally leveraging unlabeled videos (using flow supervision) for scalable visual geometry learning, enabling accurate multi-view 3D reconstruction in-the-wild.

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Shubham Tulsiani

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