In recent years, point clouds have been widely used in powerline inspection, smart cities, autonomous driving, and other fields. The deep learning-based point cloud processing methods have attracted more and more attention due to the developments of laser scanning technology and machine learning. However, the recent methods largely ignore global contextual relationships and do not make full use of the complementation between local feature and high-level geometric information. To the problem, we propose a novel deep neural network, namely, the Dense connection-based Kernel Point Network (DenseKPNet), which can greatly expand the receptive field of kernel point convolution to extract rich semantic context information and valuable geometric features from the local region effectively. Specifically, we first design a multiscale convolution kernel point module to extract initial geometric features from coarse to fine. Then, we design the dense connection module to efficiently learn more expressive local geometric features while capturing rich contextual information. In addition, we propose the kernel point convolution attention module (KPCAM), which can capture global interdependencies between points and strengthen the discriminativeness of effective features. We evaluate our method on public indoor and outdoor datasets. The qualitative and quantitative experimental results show the effectiveness of DenseKPNet. The mIoU of the proposed method on S3DIS and semantic3D datasets can reach 68.9% and 77.9%, respectively.

The accuracy with which a neural network interprets a point cloud depends on the quality of the features expressed by the network. Addressing this issue, we propose a multi-level feature extraction layer (MFEL) which collects local contextual feature and global information by modeling point clouds at different levels. The MFEL is mainly composed of three independent modules, including the aggregated GAPLayer, the spatial position perceptron, and the RBFLayer, which learn point cloud features from three different scales. The aggregated GAPLayer aggregates the geometry features of neighboring points in a local coordinate system to centroid by graph convolution. Then, the spatial position perceptron independently learns the position features of each point in the world coordinate system. Finally, the RBFLayer aggregates points into pointsets according to the correlation between features, and extracts features from the pointset scale through the quantization layer. Based on the MFEL, an end-to-end classification and segmentation network, namely the MFNet and MFNet-S, is proposed. In the proposed network, the channel-attention mechanism is employed to better aggregate multi-level features. We conduct classification and semantic segmentation experiments on four standard datasets. The results show that the proposed method outperforms the compared methods on the multiple datasets, resulting in 93.1% classification accuracy in ModelNet40. Furthermore, the mIoU of part semantic segmentation in ShapeNet is 85.4%, and the mIoU for semantic segmentation in S3DIS and Semantic3D is 62.9% and 71.9%, respectively.