VGG-T^3: Offline Feed-Forward 3D Reconstruction at Scale
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 11.6times 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
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2026-02-27T16:40:00.776Z
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Sven Elflein
sven-el
user
sven-el
69a11a51a13deaa4494489bc
Ruilong Li
69a11a51a13deaa4494489bd
Sérgio Agostinho
69a11a51a13deaa4494489be
Zan Gojcic
69a11a51a13deaa4494489bf
Laura Leal-Taixé
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Qunjie Zhou
69a11a51a13deaa4494489c1
Aljosa Osep
VGG-T^3: Offline Feed-Forward 3D Reconstruction at Scale
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 11.6times 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.
VGG-T³ addresses scalability issues in 3D reconstruction by transforming variable-length key-value representations into fixed-size MLPs through test-time training, enabling linear scaling with input views and achieving significant speedup over traditional softmax attention methods.
3D reconstruction feed-forward methods computational requirements memory requirements Key-Value space representation scene geometry Multi-Layer Perceptron test-time training Visual Geometry Grounded Test Time Training softmax attention point map reconstruction visual localization