WinT3R: Window-Based Streaming Reconstruction with Camera Token Pool
We present WinT3R, a feed-forward reconstruction model capable of online
prediction of precise camera poses and high-quality point maps. Previous
methods suffer from a trade-off between reconstruction quality and real-time
performance. To address this, we first introduce a sliding window mechanism
that ensures sufficient information exchange among frames within the window,
thereby improving the quality of geometric predictions without large
computation. In addition, we leverage a compact representation of cameras and
maintain a global camera token pool, which enhances the reliability of camera
pose estimation without sacrificing efficiency. These designs enable WinT3R to
achieve state-of-the-art performance in terms of online reconstruction quality,
camera pose estimation, and reconstruction speed, as validated by extensive
experiments on diverse datasets. Code and model are publicly available at
https://github.com/LiZizun/WinT3R.
68be737bc123124955ef60d8
Zizun Li
68be737bc123124955ef60d9
Jianjun Zhou
claimed_verified
2025-09-16T09:43:35.492Z
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Zhou
user
ZhouTimeMachine
68be737bc123124955ef60da
Yifan Wang
68be737bc123124955ef60db
Haoyu Guo
claimed_verified
2025-09-08T07:01:51.542Z
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/avatars/7978304e3fe99b0d4d0712441c6a24f3.svg
Haoyu Guo
user
ghy0324
68be737bc123124955ef60dc
Wenzheng Chang
68be737bc123124955ef60dd
Yang Zhou
68be737bc123124955ef60de
Haoyi Zhu
68be737bc123124955ef60df
Junyi Chen
68be737bc123124955ef60e0
Chunhua Shen
68be737bc123124955ef60e1
Tong He
claimed_verified
2025-09-17T13:08:42.594Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/inILqQ05sESbYLdsEldJ_.png
Tong He
user
tonghe90
WinT3R: Window-Based Streaming Reconstruction with Camera Token Pool
We present WinT3R, a feed-forward reconstruction model capable of online
prediction of precise camera poses and high-quality point maps. Previous
methods suffer from a trade-off between reconstruction quality and real-time
performance. To address this, we first introduce a sliding window mechanism
that ensures sufficient information exchange among frames within the window,
thereby improving the quality of geometric predictions without large
computation. In addition, we leverage a compact representation of cameras and
maintain a global camera token pool, which enhances the reliability of camera
pose estimation without sacrificing efficiency. These designs enable WinT3R to
achieve state-of-the-art performance in terms of online reconstruction quality,
camera pose estimation, and reconstruction speed, as validated by extensive
experiments on diverse datasets. Code and model are publicly available at
https://github.com/LiZizun/WinT3R.
WinT3R, a feed-forward reconstruction model, achieves high-quality camera pose estimation and real-time performance using a sliding window mechanism and a global camera token pool.
feed-forward reconstruction model sliding window mechanism geometric predictions compact representation global camera token pool camera pose estimation real-time performance