tttLRM：面向长上下文与自回归三维重建的测试时训练
tttLRM: Test-Time Training for Long Context and Autoregressive 3D Reconstruction
把多视角观测压缩进 TTT 层的快速权重作为隐式三维表示，再解码为高斯泼溅，为长序列/流式输入的三维重建提供线性复杂度方案；其「新视角合成预训练迁移到显式三维建模」的结论对做前馈三维重建的读者有直接参考价值。
原文依据 leverages a Test-Time Training (TTT) layer to enable long-context, autoregressive 3D reconstruction with linear computational complexity
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出 tttLRM，一种新的大规模三维重建模型，它利用测试时训练（TTT）层实现长上下文、自回归的三维重建，计算复杂度为线性，从而进一步扩展了模型能力。我们的框架高效地将多幅图像观测压缩进 TTT 层的快速权重中，在潜空间内形成一种隐式三维表示，该表示可被解码为多种显式格式，例如用于下游应用的高斯泼溅（GS）。模型的在线学习变体支持从流式观测中进行渐进式三维重建与细化。我们证明，在新视角合成任务上进行预训练能够有效迁移到显式三维建模，从而提升重建质量并加快收敛。大量实验表明，在物体与场景两类数据上，我们的方法在前馈三维高斯重建方面均优于最优方法。
英文原文摘要 996 字符
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.