A Fast Location Algorithm for Very Sparse Point Clouds Based on Object Detection
This addresses the challenge of AR object location on resource-constrained devices, but it is incremental as it combines existing methods like YOLOv3-Tiny and PCA.
The paper tackles the problem of recognizing and locating target objects in AR scenes on low-end mobile devices with monocular cameras, achieving high positioning speed and accuracy in experiments using a smartphone.
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.