GRLoR: A Unified Global Retrieval and Local Reranking Framework for 3-D Place Recognition

Three-dimensional place recognition aims to search point cloud in a large database that matches the query. It is an essential task in remote sensing applications, such as smart city management and disaster monitoring. The existing methods commonly leverage global descriptors to perform point cloud retrieval for place recognition. However, these methods rely on spatial aggregation to obtain global descriptors, which are neither discriminative nor general. In this letter, we propose a unified global retrieval and local reranking (namely, GRLoR) framework for 3-D place recognition. Specifically, we first utilize a self-attention mechanism to capture the channel dependencies of local features and design a spatial-fusion pooling (SFP) approach to obtain a discriminative global descriptor for retrieval. We then construct a feature correlation module for local reranking, which uses a cross-attention mechanism to determine whether the point cloud pair matches correctly by predicting the similarity of local regions. Experiments conducted on several public benchmarks validate the superiority performance of our method. For instance, it outperforms the strongest model by an average of about 1% on the public datasets in terms of AR@1.