一次前向传播完成千张图像的3D重建，速度显著提升。
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
- 用Transformer并行处理上千张图像，跳过逐对迭代对齐。
- 在多个数据集上实现最优精度，推理速度比现有方法快数倍。
- 适合需要快速高精度3D重建的大规模多视角应用。
多视角3D重建是计算机视觉的核心挑战，尤其在需要跨多种视角生成精确且可扩展表示的应用中。当前领先方法如DUSt3R采用基础的成对处理方式，需通过代价高昂的全局对齐过程从多视图重建。本文提出快速3D重建（Fast3R），一种对DUSt3R的新型多视角泛化方法，通过在单次前向传播中并行处理N张图像，实现高效且可扩展的3D重建。Fast3R基于Transformer架构，避免了迭代对齐需求。在相机位姿估计和3D重建上的大量实验表明，Fast3R达到最先进的性能，推理速度显著提升，误差累积大幅减少。这些结果确立了Fast3R作为多视角应用的稳健替代方案，在不牺牲重建精度的前提下实现了更强的可扩展性。
原文摘要 · Abstract (English)
Multi-view 3D reconstruction remains a core challenge in computer vision, particularly in applications requiring accurate and scalable representations across diverse perspectives. Current leading methods such as DUSt3R employ a fundamentally pairwise approach, processing images in pairs and necessitating costly global alignment procedures to reconstruct from multiple views. In this work, we propose Fast 3D Reconstruction (Fast3R), a novel multi-view generalization to DUSt3R that achieves efficient and scalable 3D reconstruction by processing many views in parallel. Fast3R's Transformer-based architecture forwards N images in a single forward pass, bypassing the need for iterative alignment. Through extensive experiments on camera pose estimation and 3D reconstruction, Fast3R demonstrates state-of-the-art performance, with significant improvements in inference speed and reduced error accumulation. These results establish Fast3R as a robust alternative for multi-view applications, offering enhanced scalability without compromising reconstruction accuracy.
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