PoCo：用于RGBD室内地点识别的点上下文聚类
PoCo: Point Context Cluster for RGBD Indoor Place Recognition
AI总结：
提出端到端算法PoCo，通过推广聚类上下文并融合颜色与几何特征，从噪声点云提取全局描述符，在ScanNet-PR和ARKit上实现SOTA性能，分别提升5.7%和13.3%，且推理更快。
摘要：
我们提出了一种新颖的端到端算法（PoCo），用于室内RGB-D地点识别任务，旨在从参考数据库中为给定的查询帧识别出最可能的匹配。该任务由于感知传感器的视野受限和感知范围有限而面临固有挑战。我们提出了一种新的网络架构，该架构推广了最近的聚类上下文（CoCs）方法，通过端到端学习直接从噪声点云中提取全局描述符。此外，我们通过将颜色和几何模态整合到点特征中来开发该架构，以增强全局描述符的表示。我们在公共数据集ScanNet-PR和ARKit上进行了评估，分别包含807和5047个场景。PoCo实现了最先进的性能：在ScanNet-PR上，我们实现了R@1为64.63%，比最佳已发表结果CGis（61.12%）提高了5.7%；在ARKit上，我们实现了R@1为45.12%，比最佳已发表结果CGis（39.82%）提高了13.3%。此外，PoCo在推理时间上比CGis具有更高的效率（快1.75倍），并且我们展示了PoCo在真实实验室环境中识别地点的有效性。
英文摘要：
We present a novel end-to-end algorithm (PoCo) for the indoor RGB-D place recognition task, aimed at identifying the most likely match for a given query frame within a reference database. The task presents inherent challenges attributed to the constrained field of view and limited range of perception sensors. We propose a new network architecture, which generalizes the recent Context of Clusters (CoCs) to extract global descriptors directly from the noisy point clouds through end-to-end learning. Moreover, we develop the architecture by integrating both color and geometric modalities into the point features to enhance the global descriptor representation. We conducted evaluations on public datasets ScanNet-PR and ARKit with 807 and 5047 scenarios, respectively. PoCo achieves SOTA performance: on ScanNet-PR, we achieve R@1 of 64.63%, a 5.7% improvement from the best-published result CGis (61.12%); on Arkit, we achieve R@1 of 45.12%, a 13.3% improvement from the best-published result CGis (39.82%). In addition, PoCo shows higher efficiency than CGis in inference time (1.75X-faster), and we demonstrate the effectiveness of PoCo in recognizing places within a real-world laboratory environment.
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