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WinT3R: Window-Based Streaming Reconstruction with Camera Token Pool

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arxiv 2509.05296 v1 pith:6PKVHVIP submitted 2025-09-05 cs.CV cs.AI

Zizun Li , Jianjun Zhou , Yifan Wang , Haoyu Guo , Wenzheng Chang , Yang Zhou , Haoyi Zhu , Junyi Chen
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Chunhua Shen Tong He
This is my paper · ORCID
classification cs.CVcs.AI
keywords camerareconstructionwint3rqualityestimationmodelonlineperformance
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Glob3R: Global Structure-from-Motion with 3D Foundation Models

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A frozen Pi3X backbone plus dense warping tracks and keyframe sliding-window global optimization yields more accurate, scalable SfM than feed-forward or classical baselines alone.

  2. TTSA3R: Training-Free Temporal-Spatial Adaptive Persistent State for Streaming 3D Reconstruction

    cs.CV 2026-01 conditional novelty 6.0 of 10

    TTSA3R adds temporal and spatial adaptive gating to the CUT3R persistent-state update rule, cutting error growth on long sequences from >4x to 1.33x over 50-250 frames.

  3. 2K Retrofit: Entropy-Guided Efficient Sparse Refinement for High-Resolution 3D Geometry Prediction

    cs.CV 2026-03 conditional novelty 5.5 of 10

    Entropy-guided sparse refinement upgrades frozen low-resolution geometric foundation models to accurate 2K depth and pointmap outputs at a fraction of full-resolution cost.

  4. IGGT4D: Streaming 4D Instance-Grounded Geometry Transformer

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A causal streaming transformer that jointly predicts camera motion, 3D geometry, and persistent object-instance features from video, trained on a new 147K-sequence 4D dataset.

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