tttLRM: Test-Time Training for Long Context and Autoregressive 3D Reconstruction
We propose tttLRM, a novel large 3D reconstruction model that leverages a Test-Time Training (TTT) layer to enable long-context, autoregressive 3D reconstruction with linear computational complexity, further scaling the model's capability. Our framework efficiently compresses multiple image observations into the fast weights of the TTT layer, forming an implicit 3D representation in the latent space that can be decoded into various explicit formats, such as Gaussian Splats (GS) for downstream applications. The online learning variant of our model supports progressive 3D reconstruction and refinement from streaming observations. We demonstrate that pretraining on novel view synthesis tasks effectively transfers to explicit 3D modeling, resulting in improved reconstruction quality and faster convergence. Extensive experiments show that our method achieves superior performance in feedforward 3D Gaussian reconstruction compared to state-of-the-art approaches on both objects and scenes.
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Chen Wang
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2026-02-24T09:50:32.196Z
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Chen Wang
chenwang
user
chenwang
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Hao Tan
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Wang Yifan
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Zhiqin Chen
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Yuheng Liu
claimed_verified
2026-02-24T09:50:29.957Z
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YuhengLiu
Yuheng02
user
Yuheng02
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Kalyan Sunkavalli
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Sai Bi
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Lingjie Liu
699d14f34e37ec6dfa1bc518
Yiwei Hu
tttLRM: Test-Time Training for Long Context and Autoregressive 3D Reconstruction
We propose tttLRM, a novel large 3D reconstruction model that leverages a Test-Time Training (TTT) layer to enable long-context, autoregressive 3D reconstruction with linear computational complexity, further scaling the model's capability. Our framework efficiently compresses multiple image observations into the fast weights of the TTT layer, forming an implicit 3D representation in the latent space that can be decoded into various explicit formats, such as Gaussian Splats (GS) for downstream applications. The online learning variant of our model supports progressive 3D reconstruction and refinement from streaming observations. We demonstrate that pretraining on novel view synthesis tasks effectively transfers to explicit 3D modeling, resulting in improved reconstruction quality and faster convergence. Extensive experiments show that our method achieves superior performance in feedforward 3D Gaussian reconstruction compared to state-of-the-art approaches on both objects and scenes.
A novel 3D reconstruction model called tttLRM uses a Test-Time Training layer to enable efficient, scalable autoregressive reconstruction with linear complexity, achieving better results than existing methods.
Test-Time Training autoregressive 3D reconstruction fast weights latent space Gaussian Splats progressive reconstruction novel view synthesis explicit 3D modeling feedforward 3D Gaussian reconstruction