Searching Efficient Dynamic Graph CNN for Point Cloud Processing

Abstract

Despite superior performance on various point cloud processing tasks, convolutional neural networks (CNN) are challenged by deploying on resource-constraint devices such as cars and cellphones. Most existing convolution variants, such as dynamic graph CNN (DGCNN), require elaborately manual design and scaling-up across various constraints to accommodate multiple hardware deployments. It results in a massive amount of computation and limits the further application of these models. To this end, we propose a one-shot neural architecture search method for point cloud processing to achieve efficient inference and storage across various constraints.We conduct our method with DGCNN to create a compressed model.Extensive experiments on the point cloud classification and part segmentation tasks strongly evidence the benefits of the proposed method. Compared with the original network, we achieve 17.5$ imes$ computation saving on the classification task with the comparable performance and obtain a 2.7$ imes$ model compression ratio on the part segmentation task with slight IoU loss.