Time-of-Flight Point Cloud Denoising Method Based on Confidence Level

The time-of-flight (ToF) 3D imaging method suffers from reduced precision in the depth measurement of target objects because of multipath interference and mixed pixels. Traditional methods improve the accuracy of the measurement by optimizing and reconstructing abnormal point cloud data or filtering noisy point cloud data. However, these methods are complex and can easily lead to excessive smoothing. The relationship between a valid point cloud and noisy point cloud in a 3D point cloud image is difficult to express using a mathematical model. To address these problems, a point cloud denoising method based on the confidence level is proposed in this paper. First, the probability correlation of multi-frame point cloud data is analyzed, and the confidence level of the point cloud data is used as the basis to distinguish valid point clouds from noisy point clouds. Second, by utilizing the vector duality between multi-frame point clouds, a fast algorithm for extracting point clouds with different confidence levels is presented, and its time complexity is O(L). Finally, the algorithm is used to extract the point cloud data with a high confidence level in multi-frame 3D images to obtain the real measurement data of the target object. We focus on the comparative experiments of four groups of point cloud data in different scenes. The experimental results show that the algorithm can not only effectively filter the noise but also significantly improve the distance measurement accuracy of the target object and enhance the characteristics of the target object; therefore, it has extensive application value.