Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization

Although re-ranking methods are widely used in many retrieval tasks to improve performance, they haven't been studied in the context of point cloud retrieval for metric localization. In this letter, we introduce Spectral Geometric Verification (SpectralGV), for the re-ranking of retrieved point clouds. We demonstrate how the optimal inter-cluster score of the correspondence compatibility graph of two point clouds can be used as a robust fitness score representing their geometric compatibility, hence allowing geometric verification without registration. Compared to the baseline geometric verification based re-ranking methods which first register all retrieved point clouds with the query and then sort retrievals based on the inlier-ratio after registration, our method is considerably more efficient and provides a deterministic re-ranking solution while remaining robust to outliers. We demonstrate how our method boosts the performance of several correspondence-based architectures across 5 different large-scale point cloud datasets. We also achieve state-of-the-art results for both place recognition and metric-localization on these datasets. To the best of our knowledge, this letter is also the first to explore re-ranking in the point cloud retrieval domain for the task of metric localization. The open-source implementation will be made available at: https://github.com/csiro-robotics/SpectralGV.