DRIM: Depth Restoration With Interference Mitigation in Multiple LiDAR Depth Cameras

Abstract

LiDAR depth cameras are widely used for accurate depth measurement in various applications. However, when multiple cameras operate simultaneously, mutual interference causes artifacts in the captured depth data, which existing image restoration methods struggle to handle. In this letter, we propose DRIM, a novel approach for real-time depth restoration under multi-device interference. Our method begins by distinguishing interference-induced artifacts, then predicts and leverages these artifacts to guide the restoration process. Since there is no existing dataset for learning interference in multiple LiDAR depth cameras, we create and provide the first depth interference dataset. Our experiments demonstrate superior depth restoration performance compared to other image restoration methods, achieving real-time processing speeds ($\approx$33 FPS) that are significantly faster than existing approaches while showing the capability to restore depth in challenging scenarios. These results demonstrate that our proposed method effectively restores interfered depth in multiple LiDAR depth cameras with practical real-time performance.