<p>LiDAR-Camera Fusion for 3D Object Detection in Bird’s-Eye-View (BEV) Representation has emerged as a pivotal research direction in autonomous driving. In existing methods, the BEV features in LiDAR-Camera fusion are significantly distorted due to errors in view conversion. In this work, we introduce WalFusion, a fast and efficient fusion detection framework. During the transformation from 2D to BEV, we address LiDAR depth semantic ambiguity and depth penetration. We introduce the wavelet transform for the first time and take advantage of its advantages in compressing and separating local and global features. Specifically, we propose the Wavelet Depth Encoder (WDE) to cleanse and encode the projected depth information from LiDAR. Subsequently, through the Wavelet Fusion Decoder (WFD), we perform multiscale separation and fusion of occluded target depths and image features. Furthermore, in the context of BEV representation, we introduce the Dynamic Global Fusion (DGF) module. This module dynamically aligns the BEV features derived from LiDAR and camera data, enhancing the overall coherence and accuracy of the fused representation. Experimental results on the nuScenes validation set demonstrate that WalFusion surpasses all comparable fusion models with near-real-time inference speed, outperforming the fastest existing fusion framework, DAL-tiny, by 1.4 NDS points. In particular, when tested with larger input sizes, WalFusion* surpasses DAL by 54% in inference speed, achieving state-of-the-art performance with 74.1 NDS.</p>