Real-Time Artifact Compensation for Depth Images of Multi-Frequency ToF

多频飞行时间相机实时深度补偿算法研究

¹ School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
² School of Computer Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Received: 6 March 2018

Abstract

During the last few years, Time-of-Flight(TOF) sensor achieved a significant impact onto research and industrial fields due to that it can capture depth easily. For dynamic scenes and phase fusion, ToF sensor's working principles can lead to significant artifacts, therefore an efficient method to combine motion compensation and kernel density estimate multi-frequency unwrapping is proposed. Firstly, the raw multi-phase images are captured, then calculate the optical flow between each frequency. Secondly, by generating multiple depth hypotheses, uses a spatial kernel density estimation is used to rank them with wrapped phase images. Finally, the accurate depth from fused phase image is gotten. The algorithm on Kinect V2 is validated and the pixel-wise part is optimized using GPU. The method shows its real time superior performance on real datasets.

摘要

针对多频飞行时间相机在曝光时间内物体运动以及相位融合中产生的偏差，提出一种基于光流以及空间核密度估计的实时补偿算法。首先获取原始的多频相位图像，计算出不同频率之间的相对光流，获得每种频率下的补偿相位；在此基础上针对不同频率的相位做出可信度假设并利用空间核密度估计对其进行排名，获得最终的相位融合图并生成对应的深度图。实验部分，使用Kinect V2相机获取的原始多频相位图对算法进行验证，同时利用GPU进行并行加速；结果表明该算法可以实时对物体平行于光轴运动的相位融合偏差进行补偿，有效地提高了深度图像的精度和稳定性。