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We present AEGIS-Net, a novel indoor place recognition model that takes inRGB point clouds and generates global place descriptors by aggregatinglower-level color, geometry features and higher-level implicit semanticfeatures. However, rather than simple feature concatenation, self-attentionmodules are employed to select the most important local features that bestdescribe an indoor place. Our AEGIS-Net is made of a semantic encoder, asemantic decoder and an attention-guided feature embedding. The model istrained in a 2-stage process with the first stage focusing on an auxiliarysemantic segmentation task and the second one on the place recognition task. Weevaluate our AEGIS-Net on the ScanNetPR dataset and compare its performancewith a pre-deep-learning feature-based method and five state-of-the-artdeep-learning-based methods. Our AEGIS-Net achieves exceptional performance andoutperforms all six methods.

AEGIS-Net: Attention-guided Multi-Level Feature Aggregation for Indoor Place Recognition