Point cloud completion aims to accurately estimate complete point clouds from partial observations. Existing methods often directly infer the missing points from the partial shape, but they suffer from limited structural information. To address this, we propose the Bilateral Coarse-to-Fine Network (BCFNet), which leverages 2D images as guidance to compensate for structural information loss. Our method introduces a multi-level codeword skip-connection to estimate structural details. Experimental results show that BCF-Net outperforms state-of-the-art point cloud completion networks on synthetic and real-world datasets.