An Improved PoinTr Point Cloud Completion Method Based on Feature Enhancement

Abstract

To address the issue that point cloud data is often incomplete and difficult to obtain, we propose a point cloud completion method to improve the PoinTr method based on feature enhancement. In dataset preprocessing, the farthest point of the original point cloud is sampled to obtain the central point coordinates. Our method constructs an MLP network, where the local information of these central points is obtained and the location embedding is performed. Combining network and SENet network, the local features of the point cloud are extracted and enhanced, and the location embedding and local features are added to obtain the point proxies of the original point cloud. Afterward, our method predicts the missing part of the point cloud by using an Encoder to model the relationship between the point cloud structure information and points, and then using a Decoder to learn the relationship between the missing and existing parts of the point cloud and reconstruct the missing point cloud. Our method also modifies the attention mechanism to make the features more global and enhance the network expression. Finally, the point cloud is refined, and is realized by predicting multiple points around each point of the coarse point cloud through the FoldingNet network, and the final output is the complete point cloud. Experimental results show that the proposed method can not only reduce the performance overhead, but also improve the effects of point cloud completion.