PoCo: Point Context Cluster for RGBD Indoor Place Recognition
Jing Liang Zhuo Deng Zheming Zhou Omid Ghasemalizadeh Dinesh Manocha Min Sun Cheng-Hao Kuo Arnie Sen
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.