A Multiscale Convolutional Neural Network With Color Vegetation Indices for Semantic Labeling of Point Cloud

This letter presents a multiscale convolutional neural network with color vegetation indices (MCCNN) for semantic labeling of point cloud directly in a 3-D model. First, color vegetation indices are calculated for each point with RGB information. Second, based on classic Point convolutional neural network (CNN), a new multiscale network is designed to incorporate multiscale information through the spatial contexts of different sizes around each point by setting different convolution of kernels $K$ , and then multiscale features produced by different convolutional layers are aggregated and unsampled. Finally, via Fully Connection layer and Softmax classifier, each point is labeled. Two different datasets, Semantic3D and Vaihingen3D, are used to evaluate the performance of the proposed method, and the results are compared with those produced by other existing approaches. Experimental results indicate that the proposed method achieves 84.5% in terms of overall accuracy on Semantic3D, and 85.2% on Vaihingen3D, which is the highest among the considered methods.