Multi-Scale Hierarchical Point Clouds Recognition Network Based on Weight Index of Point Density.

Unlike dense and regularly distributed 2D raster images, 3D point clouds are irregular and disordered, thus applying convolution on them can be difficult. A method for direct convolution of original 3D point clouds is proposed. This method uses Gaussian kernel density estimation and multi-layer perceptron (MLP) network to learn the density function. Besides, the density scale of the learning point and the relative position of the point are fitted by the weight function of the approximated MLP network to obtain the weight value of each point in the local area. The whole convolution kernel can be seen as a nonlinear function of 3D local coordinates composed of the weight function and the density function, which can be used for translation invariant and permutation invariant convolution of any set of points in 3D space. In addition, multi- scale sampling grouping and normal features are employed to achieve the best effect of this network. In the classification experiment of point cloud datasets of ModelNet40 and ModelNet10, the accuracy of the network is 92.8% and 94.7% respectively, which acquires better performance than baseline. The image datasets of CIFAR-10 and MNIST are converted into point clouds to test, experiments on aforementioned datasets show that the performance of the network in 2D images is basically equivalent to that of the traditional 2D convolutional network. [ABSTRACT FROM AUTHOR]