This paper proposes an innovative approach to Hierarchical Edge Aware 3D
Point Cloud Learning (HEA-Net) that seeks to address the challenges of noise in
point cloud data, and improve object recognition and segmentation by focusing
on edge features. In this study, we present an innovative edge-aware learning
methodology, specifically designed to enhance point cloud classification and
segmentation. Drawing inspiration from the human visual system, the concept of
edge-awareness has been incorporated into this methodology, contributing to
improved object recognition while simultaneously reducing computational time.
Our research has led to the development of an advanced 3D point cloud learning
framework that effectively manages object classification and segmentation
tasks. A unique fusion of local and global network learning paradigms has been
employed, enriched by edge-focused local and global embeddings, thereby
significantly augmenting the model's interpretative prowess. Further, we have
applied a hierarchical transformer architecture to boost point cloud processing
efficiency, thus providing nuanced insights into structural understanding. Our
approach demonstrates significant promise in managing noisy point cloud data
and highlights the potential of edge-aware strategies in 3D point cloud
learning. The proposed approach is shown to outperform existing techniques in
object classification and segmentation tasks, as demonstrated by experiments on
ModelNet40 and ShapeNet datasets.

该研究提出了一种创新的层次感知三维点云学习（HEA-Net）方法，通过关注边缘特征来解决点云数据中的噪声问题，改善物体识别和分割。该方法结合了边缘感知的概念，使用具有边缘焦点的局部和全局嵌入的本地和全局网络学习范式的独特融合，以提升对点云的分类和分割效果。实验证明，该方法在目标分类和分割任务中优于现有技术。