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Point Cloud Place recognition in Street Scenes is an essential task in many applications while existing methods face significant challenges due to the domain gap between heterogeneous Point clouds and the difficulty of feature characterization in large-scale complex Street Scenes to address these problems we propose patchogismet utilizing patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability we evaluate the performance of patch office net on a large scale heterogeneous Point cloud data set the comprehensive experimental results indicate that our method outperforms the existing state-of-the-art methods and Exhibits better generalizability [Music] foreign [Music] foreign [Music]