用稀疏关键点加速3D重建，速度提升12.4倍且精度损失小。
Speed3R: Sparse Feed-forward 3D Reconstruction Models
- 通过双分支注意力机制，仅对关键图像片段进行精细计算。
- 在千视角序列上实现12.4倍推理速度提升，几何精度损失可控。
- 适合需要快速大规模场景建模的应用，如实时导航与机器人视觉。
尽管近期前馈式3D重建模型通过单次推断联合估计密集几何与相机位姿实现了加速，但其依赖密集注意力导致复杂度呈二次增长，成为严重制约推理速度的瓶颈。为此，我们提出Speed3R，一种受结构光运动核心思想启发的端到端可训练模型：稀疏的关键点足以实现鲁棒的位姿估计。Speed3R采用双分支注意力机制，压缩分支生成粗粒度上下文先验，引导选择分支仅对最具信息量的图像标记执行细粒度注意力。该策略模仿传统关键点匹配效率，在1000视角序列上实现12.4倍推理速度提升，同时引入极小且可控的几何精度损失。在标准基准上使用VGGT与$π^3$骨干网络验证，本方法以极低计算成本实现高质量重建，为高效大规模场景建模铺平道路。
原文摘要 · Abstract (English)
While recent feed-forward 3D reconstruction models accelerate 3D reconstruction by jointly inferring dense geometry and camera poses in a single pass, their reliance on dense attention imposes a quadratic complexity, creating a prohibitive computational bottleneck that severely limits inference speed. To resolve this, we introduce Speed3R, an end-to-end trainable model inspired by the core principle of Structure-from-Motion: that a sparse set of keypoints is sufficient for robust pose estimation. Speed3R features a dual-branch attention mechanism where a compression branch creates a coarse contextual prior to guide a selection branch, which performs fine-grained attention only on the most informative image tokens. This strategy mimics the efficiency of traditional keypoint matching, achieving a remarkable 12.4x inference speedup on 1000-view sequences, while introducing a minimal, controlled trade-off in geometric accuracy. Validated on standard benchmarks with both VGGT and $π^3$ backbones, our method delivers high-quality reconstructions at a fraction of computational cost, paving the way for efficient large-scale scene modeling.
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