AEGIS-Net: Attention-guided Multi-Level Feature Aggregation for Indoor Place Recognition
This work addresses indoor place recognition for robotics and AR/VR applications, representing an incremental improvement over existing deep-learning methods.
The paper tackles indoor place recognition by proposing AEGIS-Net, which aggregates multi-level features using attention to generate global descriptors, achieving exceptional performance and outperforming six existing methods on the ScanNetPR dataset.
We present AEGIS-Net, a novel indoor place recognition model that takes in RGB point clouds and generates global place descriptors by aggregating lower-level color, geometry features and higher-level implicit semantic features. However, rather than simple feature concatenation, self-attention modules are employed to select the most important local features that best describe an indoor place. Our AEGIS-Net is made of a semantic encoder, a semantic decoder and an attention-guided feature embedding. The model is trained in a 2-stage process with the first stage focusing on an auxiliary semantic segmentation task and the second one on the place recognition task. We evaluate our AEGIS-Net on the ScanNetPR dataset and compare its performance with a pre-deep-learning feature-based method and five state-of-the-art deep-learning-based methods. Our AEGIS-Net achieves exceptional performance and outperforms all six methods.