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

DOI:
10.60864/hvtb-sh86
Citation Author(s):
Yuhang Ming, Jian Ma, Xingrui Yang, Weichen Dai, Yong Peng, Wanzeng Kong
Submitted by:
Yuhang Ming
Last updated:
6 June 2024 - 10:28am
Document Type:
Poster
Document Year:
2024
Event:
Presenters:
Yuhang Ming
Paper Code:
IVMSP-P15.3
 

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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