CSCPR：跨源上下文室内RGB-D位置识别
CSCPR: Cross-Source-Context Indoor RGB-D Place Recognition
AI总结：
本文提出CSCPR算法，通过整合全局检索与重排序处理跨源RGB-D点云，引入SCC和CSCC模块增强特征表示与匹配，并发布新数据集，在Recall@1上显著超越现有模型。
摘要：
我们扩展了之前的工作PoCo，提出了一种用于RGB-D室内位置识别的新算法——跨源上下文位置识别（CSCPR）。该算法将全局检索与重排序整合到一个端到端模型中，并保持了使用簇上下文（CoCs）进行特征处理的一致性。与以往主要在RGB域进行位置识别重排序的方法不同，CSCPR专为处理RGB-D数据而设计。我们应用CoCs来处理跨源和跨尺度的RGB-D点云，并引入了两个用于重排序的新模块：自上下文簇（SCC）和跨源上下文簇（CSCC），它们分别用于增强特征表示和基于局部特征匹配查询-数据库对。我们还发布了两个新数据集ScanNetIPR和ARKitIPR。实验表明，CSCPR在这些数据集上显著优于现有最先进模型，在ScanNet-PR数据集上的Recall@1至少提升了29.27%，在新数据集上提升了43.24%。代码和数据集将公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
