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.
@article{arxiv.2312.09538,
title = {AEGIS-Net: Attention-guided Multi-Level Feature Aggregation for Indoor Place Recognition},
author = {Yuhang Ming and Jian Ma and Xingrui Yang and Weichen Dai and Yong Peng and Wanzeng Kong},
journal= {arXiv preprint arXiv:2312.09538},
year = {2023}
}
Comments
Accepted by 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)