IgDccmNet:图像引导的双通道跨模态点云补全网络OA

Point cloud completion refers to the process of reconstructing a complete 3D shape from a partial shape.Existing multimodal point cloud completion methods primarily focus on the point cloud modality,often underutilizing the information provided by the image modality.To maximize the geometric information provided by images and generate 3D point clouds with sufficient geometric details,we propose an image-guided dual-channel cross-modal point cloud completion network,IgDccmNet.Firstly,an encoder is employed to extract features from the incomplete point cloud and the corresponding image of the complete point cloud.In the point cloud path,PointNet is used as the backbone network for point cloud feature extraction,while ResNet18 is used as the image encoder.Then,to achieve deep interaction and learning between point cloud and image features,a dual-channel cross-modal fusion module is proposed.This module strengthens the internal feature correlation of each modality via a self-attention mechanism,and establishes semantic correlations between modalities using a cross-attention mechanism,enabling effective fusion of point cloud and image features under the guidance of complementary information.Finally,a style-based point cloud decoder is designed to decode the global features and generate the predicted point cloud,which is then fused with the input incomplete point cloud.Farthest Point Sampling(FPS)is applied to obtain the completed point cloud.Experiments conducted on the ShapeNet-ViPC dataset show that the proposed method outperformsother state-of-the-art methods,achieving a 21％to 66％reduction in chamfer distance and a 4％to 78％increase in F-score.