Range-Intensity-Profile-Guided Gated Light Ranging and Imaging Based on a Convolutional Neural Network

Three-dimensional (3D) range-gated imaging can obtain high spatial resolution intensity images as well as pixel-wise depth information. Several algorithms have been developed to recover depth from gated images such as the range-intensity correlation algorithm and deep-learning-based algorithm. The traditional range-intensity correlation algorithm requires specific range-intensity profiles, which are hard to generate, while the existing deep-learning-based algorithm requires large number of real-scene training data. In this work, we propose a method of range-intensity-profile-guided gated light ranging and imaging to recover depth from gated images based on a convolutional neural network. In this method, the range-intensity profile (RIP) of a given gated light ranging and imaging system is obtained to generate synthetic training data from Grand Theft Auto V for our range-intensity ratio and semantic network (RIRS-net). The RIRS-net is mainly trained on synthetic data and fine-tuned with RIP data. The network learns both semantic depth cues and range-intensity depth cues in the synthetic data, and learns accurate range-intensity depth cues in the RIP data. In the evaluation experiments on both a real-scene and synthetic test dataset, our method shows a better result compared to other algorithms.