This paper studies the application of augmented reality real-time depth image technology to 3D human motion recognition technology. The accuracy and real-time performance of sensor-based 3D human reconstruction are affected by visual characteristics and illumination changes. Features are not easily extracted and cannot be tracked, leading to failures in the 3D reconstruction of the human body. Based on this system, the sensor-based visual inertial initialization algorithm is studied, which is integrated in the two-frame image time interval to provide accurate initial values for vision-based motion estimation, improve the accuracy of the calculated posture, and finally improve the accuracy of the 3D reconstruction system. Based on the relationship between the depth image and the distance and reflectivity, a model for correcting the distance error and reflectivity error of the depth image is established to improve the accuracy of the depth image, and finally the accuracy of the three-dimensional reconstruction of the human body.

本文研究了增强现实实时深度图像技术在3D人体运动识别技术中的应用。基于传感器的3D人体重建的准确性和实时性能受视觉特征和照明变化的影响。特征不容易提取且无法跟踪，从而导致人体3D重建失败。基于该系统，研究了基于传感器的视觉惯性初始化算法，该算法集成在两帧图像时间间隔中，可为基于视觉的运动估计提供准确的初始值，提高计算出的姿势的准确性，并最终提高3D重建系统的准确性。根据深度图像与距离和反射率之间的关系，