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.

我们提出了一种新的端到端算法（PoCo）用于室内 RGB-D 场所识别任务，旨在识别给定查询帧在参考数据库中最可能的匹配。我们提出了一种新的网络架构，通过端到端学习直接从噪声点云中提取全局描述符，并将颜色和几何模态集成到点特征中以增强全局描述符表示。我们在公共数据集 ScanNet-PR 和 ARKit 上进行了评估，结果显示 PoCo 在性能和效率方面均优于已发表的最佳结果 CGis，并证明了 PoCo 在识别实际实验室环境中的位置方面的有效性。