PUConv: Upsampling convolutional network for point cloud semantic segmentation

Due to the issue of disorder, it is difficult to directly utilise a 2D convolutional neural networks to process 3D point clouds. Recently, PointNet can directly use 3D point sets as the input of convolutional neural networks and complete the processing of point clouds with multi-layer perceptron (MLP) and symmetric functions. However, the use of MLP to compute the weight function ignores the problem of non-uniformity sampling caused by the density of point set data. To address the above problem, based on the PointNet++ structure, a kernel density estimation based method is proposed to calculate the density level of the local point sets region under the optimal bandwidth selection principle, and the density re-weighting of the weight function is developed to better fit the structure of local point clouds. In addition, the authors utilise the upsampling convolution operation to avoid duplicate storages and calculations, making the point cloud reconstruction more efficient. The experiments carried out the semantic segmentation on both the synthetic data and the real indoor scenes show that the proposed method is capable of obtaining promising semantic segmentation results.