CSCPR: Cross-Source-Context Indoor RGB-D Place Recognition
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913719057907712 |
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| author | Liang, Jing Deng, Zhuo Zhou, Zheming Sun, Min Ghasemalizadeh, Omid Kuo, Cheng-Hao Sen, Arnie Manocha, Dinesh |
| author_facet | Liang, Jing Deng, Zhuo Zhou, Zheming Sun, Min Ghasemalizadeh, Omid Kuo, Cheng-Hao Sen, Arnie Manocha, Dinesh |
| contents | We extend our previous work, PoCo, and present a new algorithm, Cross-Source-Context Place Recognition (CSCPR), for RGB-D indoor place recognition that integrates global retrieval and reranking into an end-to-end model and keeps the consistency of using Context-of-Clusters (CoCs) for feature processing. Unlike prior approaches that primarily focus on the RGB domain for place recognition reranking, CSCPR is designed to handle the RGB-D data. We apply the CoCs to handle cross-sourced and cross-scaled RGB-D point clouds and introduce two novel modules for reranking: the Self-Context Cluster (SCC) and the Cross Source Context Cluster (CSCC), which enhance feature representation and match query-database pairs based on local features, respectively. We also release two new datasets, ScanNetIPR and ARKitIPR. Our experiments demonstrate that CSCPR significantly outperforms state-of-the-art models on these datasets by at least 29.27% in Recall@1 on the ScanNet-PR dataset and 43.24% in the new datasets. Code and datasets will be released. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17457 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CSCPR: Cross-Source-Context Indoor RGB-D Place Recognition Liang, Jing Deng, Zhuo Zhou, Zheming Sun, Min Ghasemalizadeh, Omid Kuo, Cheng-Hao Sen, Arnie Manocha, Dinesh Computer Vision and Pattern Recognition Robotics We extend our previous work, PoCo, and present a new algorithm, Cross-Source-Context Place Recognition (CSCPR), for RGB-D indoor place recognition that integrates global retrieval and reranking into an end-to-end model and keeps the consistency of using Context-of-Clusters (CoCs) for feature processing. Unlike prior approaches that primarily focus on the RGB domain for place recognition reranking, CSCPR is designed to handle the RGB-D data. We apply the CoCs to handle cross-sourced and cross-scaled RGB-D point clouds and introduce two novel modules for reranking: the Self-Context Cluster (SCC) and the Cross Source Context Cluster (CSCC), which enhance feature representation and match query-database pairs based on local features, respectively. We also release two new datasets, ScanNetIPR and ARKitIPR. Our experiments demonstrate that CSCPR significantly outperforms state-of-the-art models on these datasets by at least 29.27% in Recall@1 on the ScanNet-PR dataset and 43.24% in the new datasets. Code and datasets will be released. |
| title | CSCPR: Cross-Source-Context Indoor RGB-D Place Recognition |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2407.17457 |