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arXiv · 2602.23361

VGG-T$^3$: Offline Feed-Forward 3D Reconstruction at Scale

Sven Elflein·Ruilong Li·Sérgio Agostinho·Zan Gojcic·Laura Leal-Taixé·Qunjie Zhou·Aljosa Osep
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Abstract

We present a scalable 3D reconstruction model that addresses a critical limitation in offline feed-forward methods: their computational and memory requirements grow quadratically w.r.t. the number of input images. Our approach is built on the key insight that this bottleneck stems from the varying-length Key-Value (KV) space representation of scene geometry, which we distill into a fixed-size Multi-Layer Perceptron (MLP) via test-time training. VGG-T$^3$ (Visual Geometry Grounded Test Time Training) scales linearly w.r.t. the number of input views, similar to online models, and reconstructs a $1k$ image collection in just $54$ seconds, achieving a $11.6\times$ speed-up over baselines that rely on softmax attention. Since our method retains global scene aggregation capability, our point map reconstruction error outperforming other linear-time methods by large margins. Finally, we demonstrate visual localization capabilities of our model by querying the scene representation with unseen images.

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Sven Elflein, Ruilong Li, Sérgio Agostinho, Zan Gojcic, Laura Leal-Taixé, Qunjie Zhou, Aljosa Osep. 2026-02-26. VGG-T$^3$: Offline Feed-Forward 3D Reconstruction at Scale. https://arxiv.org/abs/2602.23361

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