A Fast Location Algorithm for Very Sparse Point Clouds Based on Object Detection

TL;DR

This paper presents a fast and accurate object localization algorithm for sparse point clouds in AR scenes on low-end mobile devices, utilizing YOLOv3-Tiny and PCA for efficient detection and positioning.

Contribution

It introduces a novel combination of object detection and PCA to quickly locate objects in sparse point cloud environments on resource-constrained devices.

Findings

High positioning speed demonstrated in experiments.

Accurate object localization achieved with sparse feature points.

Effective in low-end mobile AR applications.

Abstract

Limited by the performance factor, it is arduous to recognize target object and locate it in Augmented Reality (AR) scenes on low-end mobile devices, especially which using monocular cameras. In this paper, we proposed an algorithm which can quickly locate the target object through image object detection in the circumstances of having very sparse feature points. We introduce YOLOv3-Tiny to our algorithm as the object detection module to filter the possible points and using Principal Component Analysis (PCA) to determine the location. We conduct the experiment in a manually designed scene by holding a smartphone and the results represent high positioning speed and accuracy of our method.