The rapid adoption of LiDAR sensors in autonomous driving has led to an explosion of LiDAR point cloud (LPC) data, posing substantial challenges for storage and transmission. To address the complexity of large-scale and spatially non-uniform LiDAR point clouds, we introduce a new multimodal and residual-driven scalable framework (MARSNet) for LiDAR point cloud compression (LPCC). Our MARSNet integrates an end-to-end deep network with a residual-aware compression module that leverages multiple modalities. By aligning and jointly encoding point cloud, depth, segmentation, and residual information, the proposed approach generates ultra-low-bit latent representations while preserving fine-grained geometric details. Extensive experimental validations show that MARSNet consistently outperforms 16 advanced LPCC models, achieving superior reconstruction quality at ultra-low bitrates.