LPS-Net: Lightweight Parameter-shared Network for Point Cloud-based Place Recognition

Abstract

With innovation in fields such as autonomous driving and augmented reality, point cloud-based place recognition has gained significant attention. Many methods try to address this problem by extracting and matching global descriptors in a database, but they often must balance the extraction of comprehensive contextual information and large model sizes. To overcome this challenge, we propose a lightweight parameter-shared network (LPS-Net), which includes multiple bidirectional perception units (BPUs) to extract multiscale long-range contextual information and parameter-shared NetVLADs (PS-VLADs) to aggregate descriptors. A BPU includes a parameter-shared convolution module (SharedConv) that significantly compresses the model and enhances its ability to capture informative features. In PS-VLADs, we replace half the parameters used in the original NetVLAD with trainable scalars, which further reduces the model size, and theoretically prove their equivalence. Experimental results demonstrate that LPS-Net achieves state-of-the-art performance at the task of point cloud-based place recognition while maintaining a small model size. Code and supplementary materials can be found at https://github.com/Yavinr/LPS-Net.