Compression method for three-dimensional point cloud deep model

With the widespread application of computer three-dimensional vision, point cloud processing algorithms based on deep learning have attracted a lot of research in recent years, however, the time and storage consuming characteristics have greatly restricted its deployment and application on the mobile terminal devices. Based on the general idea of improving the loss function, a new point cloud deep model compression framework was proposed, and the knowledge distillation method was introduced into the binary quantization model. At the same time, considering the speciality of the point cloud aggregation operation, an auxiliary loss item was introduced. The improved loss function includes three parts: prediction loss, distillation loss and auxiliary loss. The experimental results show that, compared with the existing algorithms, the proposed algorithm can obtain higher accuracy, meanwhile, the application to current mainstream point cloud deep network models can also achieve good scalability.