A Point Cloud Completion Method Guided by Image Information of Missing Regions

View-guided point cloud completion (ViPC) enhances completion quality by incorporating image modality, yet existing methods often introduce background interference in cross-modal attention, underutilize image cues aligned with missing regions, and employ decoders with limited structural modeling capacity. To address these limitations, in this work, RGK (Reverse to Get the Key), a cross-modal guidance module composed of Reversed Cross Attention (RevCA) and a Missing Region Image-guided Completion Decoder (MRICD) is proposed. RevCA augments standard cross-attention through point-wise dynamic similarity gating and reversed attention redistribution to emphasize features associated with missing regions. MRICD further selects an auxiliary image token sequence based on RevCA and performs cross-attention-based interaction for accurate completion. Experiments on ShapeNet-ViPC show that integrating RGK into baseline networks consistently improves performance over baselines and competing methods, demonstrating its effectiveness in extracting key cross-modal features and restoring point cloud structures.