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[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.

Shubham Tulsiani

3,569 subscribers

17,319 views • 7 months ago •via X (Twitter)

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Shubham Tulsiani7 months ago

[2/N] The key question we address is how to incorporate flow supervision. We find that a 'factored' flow prediction module encourages geometric learning (unlike a matching-based predictor) while robustly handling dynamic scenes (unlike explicit geometric projection).

Shubham Tulsiani7 months ago

[3/N] Our overall approach is very simple — leveraging a mix of labeled 3D data (via direct supervision) and unlabeled videos (via flow supervision) to train/finetune typical visual geometry prediction networks.

Shubham Tulsiani7 months ago

[4/N] We find that: a) our factored flow prediction yields clear gains over matching-based or projection-based alternatives, and that (importantly) b) performance continually improves with more unlabeled data.

Shubham Tulsiani7 months ago

[5/N] We also improve an existing large-scale model (Pi3) by jointly finetuning it on 34k labeled scenes and 800k unlabeled videos, resulting in a new state-of-the-art system.

Shubham Tulsiani7 months ago

[6/N] Our code, trained models, a large gallery of interactive examples and an online demo for you to try your own samples can be found on our project page:

Shubham Tulsiani7 months ago

[N/N] Work led by Zhongxiao (Clara) Cong ( in collaboration with Qitao Zhao and Minsik Jeon. PS: Clara is looking for PhD positions — if you want someone who can lead amazing 3D/4D projects, please reach out to her :)

BlakeTheCoder7 months ago

In-the-wild multi-view reconstruction without 3D labels. That's the jump from research demos to production systems. Checking the code now.

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161,530 views • 2 years ago

This seemingly obvious prediction didn't take long to become reality. MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors Contributions: • The first real-time SLAM system using the two-view 3D reconstruction prior MASt3R [20] as a foundation. • Efficient techniques for pointmap matching, tracking and local fusion, graph construction and loop closure, and second-order global optimization. • A state-of-the-art dense SLAM system capable of handling generic, time-varying camera models. Abstract: We present a real-time monocular dense SLAM system, designed from the ground up using MASt3R, a two-view 3D reconstruction and matching prior. Equipped with this strong prior, our system remains robust on in-the-wild video sequences, making no assumptions on a fixed or parametric camera model beyond a unique camera center. Key features include: - Efficient methods for pointmap matching, camera tracking, and local fusion - Graph construction and loop closure - Second-order global optimization With known calibration, a simple modification achieves state-of-the-art performance across various benchmarks. Altogether, we propose a plug-and-play monocular SLAM system capable of producing globally-consistent poses and dense geometry while operating at 15 FPS.

This seemingly obvious prediction didn't take long to become reality. MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors Contributions: • The first real-time SLAM system using the two-view 3D reconstruction prior MASt3R [20] as a foundation. • Efficient techniques for pointmap matching, tracking and local fusion, graph construction and loop closure, and second-order global optimization. • A state-of-the-art dense SLAM system capable of handling generic, time-varying camera models. Abstract: We present a real-time monocular dense SLAM system, designed from the ground up using MASt3R, a two-view 3D reconstruction and matching prior. Equipped with this strong prior, our system remains robust on in-the-wild video sequences, making no assumptions on a fixed or parametric camera model beyond a unique camera center. Key features include: - Efficient methods for pointmap matching, camera tracking, and local fusion - Graph construction and loop closure - Second-order global optimization With known calibration, a simple modification achieves state-of-the-art performance across various benchmarks. Altogether, we propose a plug-and-play monocular SLAM system capable of producing globally-consistent poses and dense geometry while operating at 15 FPS.

MrNeRF

29,974 views • 1 year ago