Rich and cross-fused feature embedding for few-shot point cloud semantic segmentation

Due to the limited attention given to geometric features of points in three-dimensional few-shot learning, the extracted and embedded features from point clouds lack sufficient distinction, resulting in significant intra-class variation and ambiguous inter-class differentiation of features. This hinders the development of few-shot semantic segmentation for point clouds. To address this issue, we propose a novel Feature-Rich Geometric Convolutional (FRGC) network. The FRGC executes edge and geometric convolution operations on point clouds through its feature extraction backbone network. This method models geometric relationships by capturing the distribution, magnitude, and direction of group deviations within feature vectors after extracting edge structure information. Furthermore, we introduce a cross-fusion module that employs a spatial cross-attention mechanism. This mechanism highlights regions with high spatial similarity between support and query features. By extracting discriminative features with the FRGC and facilitating targeted knowledge exchange and feature fusion between the support and query set via the cross-fusion module, we reduce intra-class variance while increasing inter-class differences, thereby significantly improving few-shot segmentation performance. Compared to state-of-the-art algorithms on the Stanford Large-Scale three-dimensional Indoor Spaces dataset and ScanNet dataset, our proposed method demonstrates performance improvements in mean Interaction over Union: 66.56% (＋3.79%) and 59.73% (＋3.22%) in 2-way 1-shot, 67.64% (−1.37%) and 60.78% (−1.32%) in 2-way 5-shot, 55.32% (＋2.54%) and 59.86% (＋4.42%) in 3-way 1-shot, 57.83% (−3.21%) and 63.61% (＋7.52%) in 3-way 5-shot.