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

Citation Author(s):
Yuhang Ming, Jian Ma, Xingrui Yang, Weichen Dai, Yong Peng, Wanzeng Kong
Submitted by:
Yuhang Ming
Last updated:
13 April 2024 - 2:58am
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Document Year:
Yuhang Ming
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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.

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