  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.
