SAGE-Net: Employing Spatial Attention and Geometric Encoding for Point Cloud Based Place Recognition

Abstract

Point cloud based place recognition remains a challenging issue due to the difficulty in extracting and encoding local features into robust and discriminative global descriptors from large-scale scenes. In this paper, we propose a novel deep neural network named SAGE-Net (Spatial Attention and Geometric Encoding Network) that incorporates both semantic and geometric features to generate discriminative and generalizable global descriptors. The spatial attention mechanism is employed to encode important inter-spatial relationships into local features. Then, the graph neural network is introduced to obtain the spatial distribution of features. Meanwhile, a geometric representation module is constructed by incorporating positional and angular information encoded in point coordinates. Then, a novel distance-based attentive pooling module is designed to extract informative local features based on the geometric distance between points. The description ability of global descriptors is improved by considering both contextual information among features and geometric features. Experiments on four benchmark datasets demonstrate that the proposed SAGE-Net outperforms the state-of-the-art approaches and shows good generalization ability for unseen scenes.