3D Point Cloud Dual Completion Network

Due to factors such as sensor performance and acquisition obstructions, continuous gaps in point cloud collection can significantly impact the representation of the form and structure of the captured object. Therefore, completing the point cloud becomes an essential task in three-dimensional point cloud analysis. Currently, employing an encoder-decoder framework to extract global features and predict complete point clouds in point cloud completion networks can disrupt the geometric structure of input point clouds, leading to displacement loss and uneven distribution of points within the cloud. To address these issues, a dual-stage point cloud completion network has been developed. The first stage generates coarse point clouds, followed by the refinement stage that produces finer point clouds. This approach incorporates a global feature perception module based on multi-head self-attention and a feature expansion module. By leveraging attention mechanisms to aggregate point cloud features, it enhances the feature dimensions of each point, effectively improving the inter-point correlations. Subsequently, within the encoder-decoder framework, these modules are utilized to enhance both the completeness and refinement of point cloud completion, effectively restoring the structure of the point cloud. Experimental tests have demonstrated that on the PCN dataset and Completion3D dataset, the average Chamfer distance achieves 5.98 × 10-3 and 6.11 × 10-3 , respectively. The visual results show superior performance compared to other methods in the visualization results. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.