Multiscale Adjacency Matrix CNN: Learning on Multispectral LiDAR Point Cloud via Multiscale Local Graph Convolution

Multispectral LiDAR can rapidly acquire 3D and spectral information of objects, providing richer features for point cloud semantic segmentation. Despite the remarkable performance of existing graph neural networks in point cloud segmentation, extracting local features still poses challenges in multispectral LiDAR point cloud scenes due to the uneven distribution of geometric and spectral information. To address the prevailing challenges, cutting-edge research predominantly focuses on extracting multi-scale local features, compensating for feature extraction shortcomings. Thus, we propose a Multi-Scale Adjacency Matrix Convolutional Neural Network (MS-AMCNN) for multispectral LiDAR point cloud segmentation. In the MS-AMCNN, a Local Adjacency Matrix Convolution Module was first proposed to efficiently leverage the point cloud's topological relationships and perceive local geometric features. Subsequently, a multi-scale feature extraction architecture was adopted to fuse local geometric features and utilize a Global Self-Attention Module to globally model the semantic features of multi-scale. The network effectively captures global and local representative features of the point cloud by harnessing the capabilities of convolutional neural networks in local feature modeling and the self-attention mechanism in global semantic feature learning. Experimental results on the Titan dataset demonstrate that the proposed MS-AMCNN network achieves a promising multispectral LiDAR point cloud segmentation performance with an overall accuracy of 94.39% and a Mean Intersection over Union(MIoU) of 86.57%. Compared to other state-of-the-art methods, such as DGCNN, which achieved an MIoU of 85.43%, and RandLA-net, with an MIoU of 85.20%, the proposed approach achieves optimal performance in segmentation.