AirRoom: Objects Matter in Room Reidentification
This addresses a challenge in augmented reality and homecare robotics by improving room reidentification, though it is incremental as it builds on existing visual place recognition methods.
The paper tackles the problem of room reidentification in cluttered indoor environments by proposing AirRoom, an object-aware pipeline that integrates multi-level object-oriented information, resulting in outperforming state-of-the-art models with improvements ranging from 6% to 80% across four datasets.
Room reidentification (ReID) is a challenging yet essential task with numerous applications in fields such as augmented reality (AR) and homecare robotics. Existing visual place recognition (VPR) methods, which typically rely on global descriptors or aggregate local features, often struggle in cluttered indoor environments densely populated with man-made objects. These methods tend to overlook the crucial role of object-oriented information. To address this, we propose AirRoom, an object-aware pipeline that integrates multi-level object-oriented information-from global context to object patches, object segmentation, and keypoints-utilizing a coarse-to-fine retrieval approach. Extensive experiments on four newly constructed datasets-MPReID, HMReID, GibsonReID, and ReplicaReID-demonstrate that AirRoom outperforms state-of-the-art (SOTA) models across nearly all evaluation metrics, with improvements ranging from 6% to 80%. Moreover, AirRoom exhibits significant flexibility, allowing various modules within the pipeline to be substituted with different alternatives without compromising overall performance. It also shows robust and consistent performance under diverse viewpoint variations.