Reconstructing Dense Depth of Dark Scenes with Sparse LiDAR, Noisy Events, and Blurry RGB

Due to the sparsity of LiDAR measurements, RGB-assisteddense depth reconstruction is widely adopted to provide structural pri-ors for autonomous driving. However, the inherent sensitivity of RGBimaging to illumination conditions makes dense depth reconstruction un-der low-light scenarios still a critical challenge. Specifically, under long-exposure imaging, motion blur in low-light RGB frames significantlydegrades the accuracy of depth reconstruction. To address this issue,we exploit the high-temporal-resolution motion cues captured by eventcameras and propose Event-guided Restoration and Upsampling Network(ERU-Net), a unified framework that tightly couples event-guided featurerestoration with depth completion. The Event-guided Feature Restora-tion (EFR) module combines implicit neural representation (INR) andself-recursive optimization to remove motion blur from long-exposure in-puts and recover artifact-free features. These features serve as structuralpriors for the Restoration-Aware Depth Upsampling (RDU) module, en-abling accurate completion of sparse LiDAR measurements with finegeometric details. Extensive experiments on challenging synthetic andreal-world captured datasets demonstrate that ERU-Net significantlyoutperforms state-of-the-art approaches, producing accurate and tem-porally consistent dense depth sequences in severe low-light conditions.Our code will be available at https://github.com/qzm777/Night-RIDE-ECCV2026.