Bilateral Coarse-to-Fine Network for Point Cloud Completion

Point cloud completion aims to accurately estimate complete point clouds from partial observations. Existing methods of-ten 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 (BCF-Net), 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.