  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.
