Accelerating SfM-based Pose Estimation with Dominating Set

TL;DR

This paper presents a graph theory-based preprocessing method using dominating sets to significantly accelerate SfM-based pose estimation, enabling real-time performance in AR, VR, and robotics with minimal accuracy loss.

Contribution

The paper introduces a novel dominating set-based preprocessing technique that speeds up SfM pose estimation without compromising accuracy.

Findings

Speed improvements of 1.5 to 14.48 times in pose estimation

Reduction of reference images by 17-23 times

Reduction of point cloud size by 2.27-4 times

Abstract

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.