Guided super-resolution of single-photon 3D LiDAR data: application to micro-scanning and 3D video upsampling

We present a statistical model for the multiscale super-resolution of complex 3D single-photon LiDAR scenes while providing uncertainty measures about the depth and reflectivity parameters. We then propose a generalization of this model by unrolling its iterations into a new deep learning architecture which requires a reduced number of trainable parameters, and provides rich information about the estimates including uncertainty measures. The proposed algorithms will be demonstrated on two specific applications: micro-scanning with a 32 × 32 time-of-flight detector array, and sensor fusion for high-resolution kilometer-range 3D imaging. Results show that the proposed algorithms significantly enhance the image quality.