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

• URL: http://arxiv.org/abs/2312.09538v1
• Date: Fri, 15 Dec 2023 05:09:08 GMT
• Title: AEGIS-Net: Attention-guided Multi-Level Feature Aggregation for Indoor Place Recognition
• Authors: Yuhang Ming, Jian Ma, Xingrui Yang, Weichen Dai, Yong Peng, Wanzeng Kong
• Abstract summary: AEGIS-Net is a novel indoor place recognition model that takes in RGB point clouds and generates global place descriptors. Our AEGIS-Net is made of a semantic encoder, a semantic decoder and an attention-guided feature embedding. 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.
• Score: 12.728087388529028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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