AN EFFICIENT DEEP UNROLLING SUPER-RESOLUTION NETWORK FOR LIDAR AUTOMOTIVE SCENES

Considering the high cost of high-resolution LiDAR sensors, in this work, a novel LiDAR super-resolution method is proposed to improve the performance on numerous autonomous vehicle perception tasks, including that of a LiDAR odometer. In more detail, we propose a regularized optimization problem employing a learnable regularizer (neural network) to capture the properties of the data. To efficiently solve this problem, a deep unrolling methodology is proposed, thus forming an interpretable and well-justified deep architecture, where its learnable parameters derived from the solution of the optimization algorithm. Extensive experiments on a real-world lidar odometry application highlight that the proposed model exhibits both superior performance as well as a significantly reduced number of trainable parameters i.e., $\mathbf{99.75\%}$ less parameters, as compared to other state-of-the-art deep learning methods.