提出SwiftVGGT，实现千米级场景高速高精度三维重建
SwiftVGGT: A Scalable Visual Geometry Grounded Transformer for Large-Scale Scenes
- 无需训练，通过Sim(3) SVD快速对齐相邻点云块
- 不依赖外部VPR模型，实现千米级场景全局一致性
- 推理时间仅为现有方法的33%，性能达当前最优
大规模场景的三维重建是三维感知的基础任务，但精度与计算效率之间的固有权衡仍是重大挑战。现有方法要么追求速度导致质量低下，要么高精度重建伴随极慢的推理速度。本文提出SwiftVGGT，一种无需训练的方法，在显著降低推理时间的同时保持高质量的稠密三维重建。为保障大规模场景的全局一致性，SwiftVGGT在不依赖外部视觉位置识别（VPR）模型的前提下实现回环闭合，消除冗余计算，支持千米级环境的准确重建。此外，我们提出一种简单而有效的点采样方法，仅需一次基于Sim(3)的奇异值分解（SVD）即可对齐相邻块，避免了以往工作中常用的迭代重加权最小二乘（IRLS）优化，带来显著提速。我们在多个数据集上评估了SwiftVGGT，结果表明其在重建质量上达到当前最优水平，同时推理时间仅为近期基于VGGT的大规模重建方法的33%。
原文摘要 · Abstract (English)
3D reconstruction in large-scale scenes is a fundamental task in 3D perception, but the inherent trade-off between accuracy and computational efficiency remains a significant challenge. Existing methods either prioritize speed and produce low-quality results, or achieve high-quality reconstruction at the cost of slow inference times. In this paper, we propose SwiftVGGT, a training-free method that significantly reduce inference time while preserving high-quality dense 3D reconstruction. To maintain global consistency in large-scale scenes, SwiftVGGT performs loop closure without relying on the external Visual Place Recognition (VPR) model. This removes redundant computation and enables accurate reconstruction over kilometer-scale environments. Furthermore, we propose a simple yet effective point sampling method to align neighboring chunks using a single Sim(3)-based Singular Value Decomposition (SVD) step. This eliminates the need for the Iteratively Reweighted Least Squares (IRLS) optimization commonly used in prior work, leading to substantial speed-ups. We evaluate SwiftVGGT on multiple datasets and show that it achieves state-of-the-art reconstruction quality while requiring only 33% of the inference time of recent VGGT-based large-scale reconstruction approaches.
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