Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis

TL;DR

This paper introduces DNDFN, a novel neural network architecture that improves 3D point cloud classification, especially in non-idealized scenarios, by fusing global and local neighborhood features and learning edge information.

Contribution

The paper proposes DNDFN with TN-Learning and IT-Conv, enhancing feature representation and reasoning in non-idealized point cloud classification tasks.

Findings

DNDFN achieves state-of-the-art results on benchmark datasets.

The method effectively handles non-idealized point clouds.

Extensive experiments validate the superiority of DNDFN.

Abstract

Recently, deep neural networks have made remarkable achievements in 3D point cloud classification. However, existing classification methods are mainly implemented on idealized point clouds and suffer heavy degradation of per-formance on non-idealized scenarios. To handle this prob-lem, a feature representation learning method, named Dual-Neighborhood Deep Fusion Network (DNDFN), is proposed to serve as an improved point cloud encoder for the task of non-idealized point cloud classification. DNDFN utilizes a trainable neighborhood learning method called TN-Learning to capture the global key neighborhood. Then, the global neighborhood is fused with the local neighbor-hood to help the network achieve more powerful reasoning ability. Besides, an Information Transfer Convolution (IT-Conv) is proposed for DNDFN to learn the edge infor-mation between point-pairs and benefits the feature transfer procedure. The transmission of information in IT-Conv is similar to the propagation of information in the graph which makes DNDFN closer to the human reasoning mode. Extensive experiments on existing benchmarks especially non-idealized datasets verify the effectiveness of DNDFN and DNDFN achieves the state of the arts.