A Point Is A Wave: Point-wave Network for Place Recognition

Point cloud place recognition is key to auto-driving, navigation, localization, and robotics. It targets finding a similar scene of the query in the database via extracted compact point features. The core challenge focuses on obtaining descriptive features to enhance retrieval performance. Existing methods concentrate on multi-layer perception with intricate architectures, needing lots of parameters to learn with limited gains. Unlike these methods, we propose an innovative approach by designing a point-wave module, modeling a point as a wave function to avoid losing the information of origin points. In this way, it dynamically promotes intercommunication among point features to advance the ultimate performance. Meanwhile, our designed point-wave architecture benefits the existing point-based methods to improve performance and save half convergence time with fewer learned parameters. Experiments on four datasets also show that the proposed method brings performance gains and is an easy plug-and-play with a lightweight property.