A real-time ToF depth optimization method based on amplitude maps

At present, ToF (Time of Flight) 3D imaging technology has broad application prospects in visual tasks such as face detection, 3D object recognition, and 3D reconstruction. However, the depth information obtained with ToF cameras often suffers from noise interference related to pixels, temperature, depth distortion, multipath interference, and background light. The existing ToF optimization algorithms are time-consuming and difficult to preserve the detailed information of the target, which seriously affects the practical application of ToF cameras. In response to the above issues, this article proposes a real-time ToF depth map optimization method based on amplitude maps. Firstly, amplitude images with noise are generated from the raw data collected by the ToF receiver. For the noise in the amplitude map, a fast and efficient bilateral grid filter is selected to denoise the amplitude map. Then, the optimized amplitude map is used to generate a mask to segment the foreground and background regions in the depth map. At the same time, the noise and error pixels in the depth map are optimized by filtering, and finally, the optimized depth map and mask are fused to generate the final depth map. The experimental results show that the algorithm proposed in this paper can effectively filter out depth map noise in real-time, remove background noise interference, and preserve the detailed information of the target object in the depth map. Helping ToF cameras have a wider range of application scenarios.