WinT3R: Window-Based Streaming Reconstruction with Camera Token Pool
Authors: name: Zizun Li, name: Jianjun Zhou, name: Yifan Wang, name: Haoyu Guo, name: Wenzheng Chang, name: Yang Zhou, name: Haoyi Zhu, name: Junyi Chen, name: Chunhua Shen, name: Tong He

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.