Dynamic single-photon 3D imaging with a sparsity-based neural network

Abstract

Deep learning is emerging as an important tool for single-photon light detection and ranging (LiDAR) with high photon efficiency and image reconstruction quality. Nevertheless, the existing deep learning methods still suffer from high memory footprint and low inference speed, which undermine their compatibility when it comes to dynamic and long-range imaging with resource-constrained devices. By exploiting the sparsity of the data, we proposed an efficient neural network architecture which significantly reduces the storage and computation overhead by skipping the inactive sites with no photon counts. In contrast with the state-of-the-art deep learning methods, our method supports one-shot processing of data frames with high spatial resolution, and achieves over 90% acceleration in computation speed without sacrificing the reconstruction quality. In addition, the speed of our method is not sensitive to the detection distance. The experiment results on public real-world dataset and our home-built system have demonstrated the outstanding dynamic imaging capability of the algorithm, which is orders of magnitude faster than the competing methods and does not require any data pruning for hardware compatibility.