用图论中的支配集加速三维姿态估计，速度提升1.5至14.48倍。
Accelerating SfM-based Pose Estimation with Dominating Set
- 基于图论的支配集预处理，筛选关键帧与点云
- 速度提升1.5~14.48倍，参考图像减少17~23倍
- 适合实时应用如AR/VR、机器人视觉系统
本文提出一种加速基于结构光运动（SfM）的姿态估计的预处理技术，对增强现实（AR）、虚拟现实（VR）和机器人等实时应用至关重要。该方法利用图论中的支配集概念对SfM模型进行预处理，显著提升姿态估计速度而几乎不损失精度。在OnePose数据集上，我们评估了多种SfM姿态估计方法，结果表明处理速度提升1.5至14.48倍，参考图像数量减少17至23倍，点云规模缩小2.27至4倍。该工作为高效且准确的3D姿态估计提供了可行方案，在实时应用中实现速度与精度的平衡。
原文摘要 · Abstract (English)
This paper introduces a preprocessing technique to speed up Structure-from-Motion (SfM) based pose estimation, which is critical for real-time applications like augmented reality (AR), virtual reality (VR), and robotics. Our method leverages the concept of a dominating set from graph theory to preprocess SfM models, significantly enhancing the speed of the pose estimation process without losing significant accuracy. Using the OnePose dataset, we evaluated our method across various SfM-based pose estimation techniques. The results demonstrate substantial improvements in processing speed, ranging from 1.5 to 14.48 times, and a reduction in reference images and point cloud size by factors of 17-23 and 2.27-4, respectively. This work offers a promising solution for efficient and accurate 3D pose estimation, balancing speed and accuracy in real-time applications.
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