Recent advances in autonomous driving highlight the complementary strengths of LiDAR and 4D Radar, yet effective fusion of these modalities remains challenging due to discrepancies in resolution, noise, and sensing characteristics. In this paper, we propose a novel plug-and-play pipeline for LiDAR and 4D Radar fusion aimed at enhancing 3D object detection performance. The framework comprises three key modules: (1) a LiDAR–4D Radar Cross-Attention module that integrates a Cross-Attention Block and a LiDAR Denoise Layer to effectively exploit complementary features across modalities while suppressing LiDAR noise; (2) a LiDAR Denoise Layer that further refines LiDAR representations using radar-guided filtering; and (3) a Dual-Channel Attention Fusion mechanism that adaptively combines the enhanced LiDAR and 4D Radar features. Extensive experiments are conducted on six baselines, evaluating both 3D and bird’s-eye view (BEV) detection outputs. Our method consistently outperforms existing approaches, achieving up to a 13.48 mAP improvement over baselines.