基于深度学习的增强现实跟踪配准方法

Xingya Yan, Guangrui Bai, Chaobao Tang
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引用次数: 0

摘要

增强现实是一种可以进行人机交互的三维可视化技术。虚拟信息被放置在现实世界的指定区域,以增强现实世界的信息。本文在现有增强现实实现流程的基础上,针对增强现实方法在复杂背景、光照变化、局部遮挡等情况下无标记定位不准确、模型漂移等问题,提出了一种基于深度学习的增强现实方法。该方法采用轻量级SSD模型进行目标检测,SURF算法提取特征点,FLANN算法进行特征匹配。实验结果表明,该方法在保证增强现实系统运行效率的同时,能有效解决特定情况下的定位不准确和模型漂移问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Augmented Reality Tracking Registration Method Based on Deep Learning
Augmented reality is a three-dimensional visualization technology that can carry out human-computer interaction. Virtual information is placed in the designated area of the real world to enhance real-world information. Based on the existing implementation process of augmented reality, this paper proposes an augmented reality method based on deep learning, aiming at the inaccurate positioning and model drift of the augmented reality method without markers in complex backgrounds, light changes, and partial occlusion. The proposed method uses the lightweight SSD model for target detection, the SURF algorithm to extract feature points and the FLANN algorithm for feature matching. Experimental results show that this method can effectively solve the problems of inaccurate positioning and model drift under particular circumstances while ensuring the operational efficiency of the augmented reality system.
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