FOTV-HQS: A Fractional-Order Total Variation Model for LiDAR Super-Resolution with Deep Unfolding Network

Huiying Xi, Xia Yuan, Shiwei Wu, Runze Geng, Kaiyang Wang, Yongshun Liang, Chunxia Zhao; Proceedings of the Asian Conference on Computer Vision (ACCV), 2024, pp. 4034-4050

LiDAR super-resolution can improve the quality of point cloud data, which is critical for improving many downstream tasks such as object detection, identification, and tracking. Traditional LiDAR super-resolution models often struggle with issues like block artifacts, staircase edges, and misleading edges. To address these challenges, a novel super-resolution model of LiDAR based on fractional-order total variation (FOTV) is proposed in this paper. We propose a FOTV regularization optimization problem, utilizing an end-to-end trainable iterative network to capture data attributes.This enables the precise reconstruction of fine details and complex structures in point clouds. Specifically, the half quadratic splitting algorithm divides the problem into data fidelity and prior regularization subproblems. We then propose a deep unfolding network, which iteratively deals with the two subproblems within the FOTV-HQS framework. Numerous experiments have shown that our approach significantly reduces the number of parameters by up to 99.68% and maintains good performance, making it ideal for applications with limited compute and storage resources.