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Introducing 👀Stereo4D👀 A method for mining 4D from internet stereo videos. It enables large-scale, high-quality, dynamic, metric 3D reconstructions, with camera poses and long-term 3D motion trajectories. We used Stereo4D to make a dataset of over 100k real-world 4D scenes.

Linyi Jin

1,008 subscribers

94,683 görüntüleme • 1 yıl önce •via X (Twitter)

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Linyi Jin profil fotoğrafı

Linyi Jin1 yıl önce

This type of data is ideal for learning the structure and dynamics of the real world. We gave this a shot — extending DUSt3R to model 3D motion, and training on our dataset. Given a pair of frames, our model predicts a 3D point cloud, and corresponding 3D motion trajectories.

Linyi Jin profil fotoğrafı

Linyi Jin1 yıl önce

See more scenes & details of how it works on our website: Paper: Thanks to the great team! Richard Tucker, @zhengqi_li, David Fouhey, @Jimantha, @holynski_ Please stay tuned for updates on data & code.

Sherwin Bahmani profil fotoğrafı

Sherwin Bahmani1 yıl önce

Congrats, will be super useful for the 4D reconstruction/generation community!!

Chen Wang profil fotoğrafı

Chen Wang1 yıl önce

Very amazing work! Wondering how to predict the 3D point trajectory of a video after pairwise prediction of DynaDust3r?

Linyi Jin profil fotoğrafı

Linyi Jin1 yıl önce

Thanks Chen! We've tried using DynaDust3r to predict motion at any time between two frames. We haven't tried to extend it to take all frames of a video, but could be a cool future direction.

Pedro Milcent profil fotoğrafı

Pedro Milcent1 yıl önce

Excellent paper @jin_linyi ! Exciting potential for models leveraging multi-modal data 🦾

IceTTT profil fotoğrafı

IceTTT1 yıl önce

Very interesting work!

Dejvuzzz profil fotoğrafı

Dejvuzzz1 yıl önce

This should be Good for 3D tracking . @jin_linyi . both 3D camera tracking and 3D mesh tracking.

Feiyu Yao profil fotoğrafı

Feiyu Yao1 yıl önce

great work! when will it be released? can't wait to use it.

☀️ Leon-Gerard Vandenberg 🇺🇸 🇳🇱 🇨🇦 🇦🇺 profil fotoğrafı

☀️ Leon-Gerard Vandenberg 🇺🇸 🇳🇱 🇨🇦 🇦🇺1 yıl önce

🤩 wow 🤩

☀️ Leon-Gerard Vandenberg 🇺🇸 🇳🇱 🇨🇦 🇦🇺 profil fotoğrafı

☀️ Leon-Gerard Vandenberg 🇺🇸 🇳🇱 🇨🇦 🇦🇺1 yıl önce

The world simulation and the singularity is near @PeterDiamandis @salimismail

Nikodeam.eth profil fotoğrafı

Nikodeam.eth1 yıl önce

Me patiently waiting for code

Time is Light profil fotoğrafı

Time is Light1 yıl önce

@zhengqi_li Time is Light !

HU Wenbo profil fotoğrafı

HU Wenbo1 yıl önce

Cool!

Maya N profil fotoğrafı

Maya N1 yıl önce

I'm blown away by the scale of your dataset! How do you plan to make it available for others to use?

Bilawal Sidhu profil fotoğrafı

Bilawal Sidhu1 yıl önce

Really cool to see VR180 videos have this impact -- this'll be huge for the computer vision community!

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A video is compressed 4D data. 🌍 Today, we’re sharing a preview of our latest research: 4D reconstruction from a single monocular video. 🎬 From one casually captured video, our system reconstructs a dynamic, freely viewable 4D Gaussian scene—recovering geometry, appearance, camera motion, and object motion across space and time. No multi-camera rig. Just video → 4DGS. The world has already been captured in video. The next step is to unlock the 4D structure inside it. Watch the result below. 👇 #4DGS #GaussianSplatting #CVPR #CVPR2026 #AIGC #AI #3DGS

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DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision paper page: We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to either synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation.

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