用测试时训练实现线性时间3D重建，700帧10秒完成。
ZipMap: Linear-Time Stateful 3D Reconstruction via Test-Time Training
- 通过测试时训练将图像集压缩为紧凑场景状态
- 单次前向传播完成700帧重建，速度超现有方法20倍
- 适合实时场景查询与流式序列重建任务
前馈Transformer模型推动了3D视觉的快速发展，但当前顶尖方法如VGGT和$π^3$的计算开销随输入图像数量呈二次增长，难以处理大规模图像集合。顺序重建方法虽降低开销，却牺牲重建质量。我们提出ZipMap，一种具有状态的前馈模型，实现线性时间、双向3D重建，同时达到或超越二次时间方法的精度。ZipMap利用测试时训练层，仅需一次前向传播即可将整个图像集合压缩为紧凑的隐藏场景状态，可在单张H100 GPU上于10秒内完成超过700帧的重建，速度比现有最优方法快20倍以上。此外，我们验证了状态化表示在实时场景查询中的优势，并拓展至序列流式重建应用。
原文摘要 · Abstract (English)
Feed-forward transformer models have driven rapid progress in 3D vision, but state-of-the-art methods such as VGGT and $π^3$ have a computational cost that scales quadratically with the number of input images, making them inefficient when applied to large image collections. Sequential-reconstruction approaches reduce this cost but sacrifice reconstruction quality. We introduce ZipMap, a stateful feed-forward model that achieves linear-time, bidirectional 3D reconstruction while matching or surpassing the accuracy of quadratic-time methods. ZipMap employs test-time training layers to zip an entire image collection into a compact hidden scene state in a single forward pass, enabling reconstruction of over 700 frames in under 10 seconds on a single H100 GPU, more than $20\times$ faster than state-of-the-art methods such as VGGT. Moreover, we demonstrate the benefits of having a stateful representation in real-time scene-state querying and its extension to sequential streaming reconstruction.
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