Edge Aware Learning for 3D Point Cloud
- URL: http://arxiv.org/abs/2309.13472v2
- Date: Wed, 25 Oct 2023 11:30:04 GMT
- Title: Edge Aware Learning for 3D Point Cloud
- Authors: Lei Li
- Abstract summary: This paper proposes an innovative approach to Hierarchical Edge Aware 3D Point Cloud Learning (HEA-Net)
It seeks to address the challenges of noise in point cloud data, and improve object recognition and segmentation by focusing on edge features.
We present an innovative edge-aware learning methodology, specifically designed to enhance point cloud classification and segmentation.
- Score: 8.12405696290333
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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