Long short‐distance topology modelling of 3D point cloud segmentation with a graph convolution neural network

Abstract

3D point cloud segmentation is a non-trivial problem due to its irregular, sparse, and unordered data structure. Existing methods only consider structural relationships of a 3D point and its spatial neighbours. However, the inner-point interactions and long-distance context of a 3D point cloud have been less investigated. In this study, we propose an effective plug-and-play module called the Long Short-Distance Topologically Modelled (LSDTM) Graph Convolutional Neural Network (GCNN) to learn the underlying structure of 3D point clouds. Specifically, we introduce the concept of subgraph to model the contextual-point relationships within a short distance. Then the proposed topology can be reconstructed by recursive aggregation of subgraphs, and importantly, to propagate the contextual scope to a long range. The proposed LSDTM can parse the point cloud data with maximisation of preserving the geometric structure and contextual structure, and the topological graph can be trained end-to-end through a seamlessly integrated GCNN. We provide a case study of triple-layer ternary topology and experimental results on ShapeNetPart, Stanford 3D Indoor Semantics and ScanNet datasets, indicating a significant improvement on the task of 3D point cloud segmentation and validating the effectiveness of our research.

Weighted graph convolutional networks based on network node degree and efficiency

“…In recent years, convolutional neural network (CNN) has developed rapidly and attracted extensive attention due to its powerful modeling ability (Zhou et al, 2017). Compared with traditional methods, the arrival of CNN brings new solutions to image processing (Zhang et al, 2019), natural language processing and other fields, such as machine translation (Hu et al, 2015), image recognition (He et al, 2016) and speech recognition (Hinton et al, 2012).…”