CSCPR: Cross-Source-Context Indoor RGB-D Place Recognition
<|reference_start|>CSCPR: Cross-Source-Context Indoor RGB-D Place Recognition: We present a new algorithm, Cross-Source-Context Place Recognition (CSCPR), for RGB-D indoor place recognition that integrates global retrieval and reranking into a single end-to-end model. Unlike prior approaches that primarily focus on the RGB domain, CSCPR is designed to handle the RGB-D data. We extend the Context-of-Clusters (CoCs) for handling noisy colorized point clouds and introduce two novel modules for reranking: the Self-Context Cluster (SCC) and Cross Source Context Cluster (CSCC), which enhance feature representation and match query-database pairs based on local features, respectively. We also present two new datasets, ScanNetIPR and ARKitIPR. Our experiments demonstrate that CSCPR significantly outperforms state-of-the-art models on these datasets by at least 36.5% in Recall@1 at ScanNet-PR dataset and 44% in new datasets. Code and datasets will be released.<|reference_end|>
@article{liang2024cscpr:,
    title={CSCPR: Cross-Source-Context Indoor RGB-D Place Recognition},
    author={Jing Liang, Zhuo Deng, Zheming Zhou, Min Sun, Omid Ghasemalizadeh,
  Cheng-Hao Kuo, Arnie Sen, Dinesh Manocha},
    journal={arXiv preprint arXiv:2407.17457},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17457},
    primaryClass={cs.CV cs.RO}
}