AEGIS-Net：注意機構による多層特徴集約を用いた屋内場所認識
AEGIS-Net: Attention-guided Multi-Level Feature Aggregation for Indoor Place Recognition
RGB点群から色・形状・意味特徴を注意機構で集約し、屋内場所を認識するグローバル記述子を生成するモデルを提案。ScanNetPRで既存手法を上回る性能を示した。
著者: Yuhang Ming, Jian Ma, Xingrui Yang, Weichen Dai, Yong Peng, Wanzeng Kong
分類: cs.CV, cs.RO
原文アブストラクト
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.
