CSCPR: Cross-Source-Context Indoor RGB-D Place Recognition

• URL: http://arxiv.org/abs/2407.17457v1
• Date: Wed, 24 Jul 2024 17:50:00 GMT
• Title: CSCPR: Cross-Source-Context Indoor RGB-D Place Recognition
• Authors: Jing Liang, Zhuo Deng, Zheming Zhou, Min Sun, Omid Ghasemalizadeh, Cheng-Hao Kuo, Arnie Sen, Dinesh Manocha,
• Abstract summary: We present a new algorithm, Cross-Source-Context Place Recognition (CSCPR), for RGB-D indoor place recognition. 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.
• Score: 47.12179061883084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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