LONG3R: Long Sequence Streaming 3D Reconstruction

• URL: http://arxiv.org/abs/2507.18255v1
• Date: Thu, 24 Jul 2025 09:55:20 GMT
• Title: LONG3R: Long Sequence Streaming 3D Reconstruction
• Authors: Zhuoguang Chen, Minghui Qin, Tianyuan Yuan, Zhe Liu, Hang Zhao,
• Abstract summary: Long3R is a novel model designed for streaming multi-view 3D scene reconstruction over longer sequences.<n>Our model achieves real-time processing by operating recurrently, maintaining and updating memory with each new observation.<n>Experiments demonstrate that LONG3R outperforms state-of-the-art streaming methods, particularly for longer sequences.
• Score: 29.79885827038617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in multi-view scene reconstruction have been significant, yet existing methods face limitations when processing streams of input images. These methods either rely on time-consuming offline optimization or are restricted to shorter sequences, hindering their applicability in real-time scenarios. In this work, we propose LONG3R (LOng sequence streaming 3D Reconstruction), a novel model designed for streaming multi-view 3D scene reconstruction over longer sequences. Our model achieves real-time processing by operating recurrently, maintaining and updating memory with each new observation. We first employ a memory gating mechanism to filter relevant memory, which, together with a new observation, is fed into a dual-source refined decoder for coarse-to-fine interaction. To effectively capture long-sequence memory, we propose a 3D spatio-temporal memory that dynamically prunes redundant spatial information while adaptively adjusting resolution along the scene. To enhance our model's performance on long sequences while maintaining training efficiency, we employ a two-stage curriculum training strategy, each stage targeting specific capabilities. Experiments demonstrate that LONG3R outperforms state-of-the-art streaming methods, particularly for longer sequences, while maintaining real-time inference speed. Project page: https://zgchen33.github.io/LONG3R/.

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