MBBOS-GCN: minimum bounding box over-segmentation—graph convolution 3D point cloud deep learning model

Abstract

Point cloud data with high accuracy and high density is an important data source for the depiction of real ground objects, and there is a broad research prospect of using point cloud data directly for 3D object detection and recognition using deep learning methods. However, many deep learning models in previous research ignored the point cloud structure information and the sampling randomness. To overcome this limitation, we proposed an innovative 3D point cloud deep learning model, namely, the minimum bounding box over-segmentation–graph convolution 3D point cloud deep learning network model (MBBOS-GCN) for enhancing the structural information perception capability of the model and reduce the sampling randomness. In MBBOS-GCN, the number of points sampled is used as the scale, and a modified graph convolution model is used to collect point cloud structure information from different scales. The point cloud is divided into several small regions by the minimum bounding box algorithm, and the farthest point sampling (FPS) algorithm is used to sample within each small region to reduce sampling randomness. The experiments on object classification and semantic scene data segmentation show that: (1) the MBBOS-GCN model has high classification and segmentation accuracy, which is up to 91.87% and 89.5% on the ModelNet40 dataset and ScanNet dataset, respectively; (2) the MBBOS-GCN model is provided has good stability and robustness with a little change in accuracy under the altering density of input point cloud data, and slight classification loss value; (3) the MBBOS-GCN model can be adapted to real complex scenes when the classification accuracy reaches up to 97.53%. These superior performance of the MBBOS-GCN model can provide an effective support for the construction of digital twin city background data and the calibration of multimode satellite feature inversion algorithm validation.