GSSnowflake: Point Cloud Completion by Snowflake with Grouped Vector and Self-Positioning Point Attention

Abstract

Point clouds are essential 3D data representations utilized across various disciplines, often requiring point cloud completion methods to address inherent incompleteness. Existing completion methods like SnowflakeNet only consider local attention, lacking global information of the complete shape, and tend to suffer from overfitting as the model depth increases. To address these issues, we introduced self-positioning point-based attention to better capture complete global contextual features and designed a Channel Attention module for adaptive feature adjustment within the global vector. Additionally, we implemented a vector attention grouping strategy in both the skip-transformer and self-positioning point-based attention to mitigate overfitting, improving parameter efficiency and generalization. We evaluated our method on the PCN dataset as well as the ShapeNet55/34 datasets. The experimental results show that our method achieved an average CD-L1 of 7.09 and average CD-L2 scores of 8.0, 7.8, and 14.4 on the PCN, ShapeNet55, ShapeNet34, and ShapeNet-unseen21 benchmarks, respectively. Compared to SnowflakeNet, we improved the average CD by 1.6%, 3.6%, 3.7%, and 4.6% on the corresponding benchmarks, while also reducing complexity and computational costs and accelerating training and inference speeds. Compared to other existing point cloud completion networks, our method also achieves competitive results.