  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.
