iLRM通过迭代优化实现高效3D高保真重建，突破多视角渲染的计算瓶颈。
iLRM: An Iterative Large 3D Reconstruction Model
- 采用迭代精修机制，分阶段融合多视角信息，降低注意力计算开销。
- 在RE10K和DL3DV数据集上，重建质量超越现有方法，速度提升显著。
- 适合追求实时高精度3D建模的工业应用与虚拟现实场景。
前馈式3D建模已成为快速高质量重建的有前景方向，尤其是直接生成显式3D表示（如3D Gaussian splatting）因渲染速度快、质量高而备受关注。然而，多数基于Transformer的方法依赖多视角图像标记间的全注意力机制，导致可扩展性差，随着视角数量或图像分辨率增加，计算成本急剧上升。为此，我们提出一种迭代式大型3D重建模型（iLRM），通过三个核心原则实现高效建模：（1）将场景表示与输入图像解耦，实现紧凑的3D表示；（2）将全局多视角交互分解为两阶段注意力机制，显著降低计算开销；（3）在每一层注入高分辨率信息，保障高保真重建。在广泛使用的RE10K和DL3DV数据集上的实验表明，iLRM在重建质量和速度方面均优于现有方法。
原文摘要 · Abstract (English)
Feed-forward 3D modeling has emerged as a promising approach for rapid and high-quality 3D reconstruction. In particular, directly generating explicit 3D representations, such as 3D Gaussian splatting, has attracted significant attention due to its fast and high-quality rendering. However, many state-of-the-art methods, primarily based on transformer architectures, suffer from severe scalability issues because they rely on full attention across image tokens from multiple input views, resulting in prohibitive computational costs as the number of views or image resolution increases. Toward a scalable and efficient feed-forward 3D reconstruction, we introduce an iterative Large 3D Reconstruction Model (iLRM) that generates 3D Gaussian representations through an iterative refinement mechanism, guided by three core principles: (1) decoupling the scene representation from input images to enable compact 3D representations; (2) decomposing global multi-view interactions into a two-stage attention scheme to reduce computational costs; and (3) injecting high-resolution information at every layer to achieve high-fidelity reconstruction. Experimental results on widely used datasets, such as RE10K and DL3DV, demonstrate that iLRM outperforms existing methods in both reconstruction quality and speed.
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