A Panoramic Segmentation Network for Point Cloud

Abstract Scene segmentation mainly consists of semantic segmentation and instance segmentation. The latest research points out that combining the two segmentation methods to achieve panoramic segmentation can understand the current scene better. The point cloud contains rich spatial information, but panoramic segmentation research in this field is rarely discussed. How to use the unified model framework to obtain the results of instance segmentation and semantic segmentation is the key to realize the task of point cloud panoramic segmentation. In this paper, we propose a panoramic segmentation network for point cloud. In feature encoding stage, we introduce the potential correlation information among points to improve the performance of feature extraction. Then, an output module is presented to combine the results of the two decoders which uses objective distance to enhance the semantic and instance segmentation. Experiments show that our model has good performance on the panoramic segmentation task of point cloud.