M²G-Net: Multimodal Mutual-Guidance Network for LiDAR Depth and Intensity Completion

Abstract

Autonomous driving has rapidly advanced with diverse sensors, especially Light Detection and Ranging (LiDAR), which provides precise geometry for tasks like simultaneous localization and mapping (SLAM). Recently, the performance of LiDAR-based SLAM has improved through studies leveraging intensity as a complementary cue to depth. However, in urban environments, dynamic objects occlude static scenes, degrading the stability and accuracy of LiDAR-based SLAM. While previous studies have focused mainly on completing occluded depth, they often disregard intensity, assuming it to be less critical or difficult to estimate due to inherent noise. This overlooks the strong complementary relationship between the two modalities, which can be exploited for effective multimodal completion. Furthermore, completing intensity alongside depth enables broader applicability to intensity-aware perception tasks. To address this issue, a Multimodal Mutual-Guidance (M 2 G) module is proposed for the joint completion of occluded depth and intensity in LiDAR data. M 2 G is integrated into a deep learning-based network that takes projected range and intensity images as input, enabling progressive cross-modal feature interaction. Leveraging the shared origin of LiDAR depth and intensity, M 2 G balances noisy intensity and smooth depth via attention and structure-aware guidance. Experimental results demonstrate that the proposed method outperforms existing inpainting and depth completion approaches, validating its effectiveness for LiDAR completion.