Point cloud based retrieval for place recognition is still a challenging problem due to drastic appearance and illumination changes of scenes in changing environments. Existing deep learning based global descriptors for the retrieval task usually consume a large amount of computation resources (e.g., memory), which may not be suitable for the cases of limited hardware resources. In this paper, we develop an efficient point cloud learning network (EPC-Net) to form a global descriptor for visual place recognition, which can obtain good performance and reduce computation memory and inference time. First, we propose a lightweight but effective neural network module, called ProxyConv, to aggregate the local geometric features of point clouds. We leverage the spatial adjacent matrix and proxy points to simplify the original edge convolution for lower memory consumption. Then, we design a lightweight grouped VLAD network (G-VLAD) to form global descriptors for retrieval. Compared with the original VLAD network, we propose a grouped fully connected (GFC) layer to decompose the high-dimensional vectors into a group of low-dimensional vectors, which can reduce the number of parameters of the network and maintain the discrimination of the feature vector. Finally, to further reduce the inference time, we develop a simple version of EPC-Net, called EPC-Net-L, which consists of two ProxyConv modules and one max pooling layer to aggregate global descriptors. By distilling the knowledge from EPC-Net, EPC-Net-L can obtain discriminative global descriptors for retrieval. Extensive experiments on the Oxford dataset and three in-house datasets demonstrate that our proposed method can achieve state-of-the-art performance with lower parameters, FLOPs, and runtime per frame.

翻译：由于环境变化中的场景出现剧烈的外观和光化变化,基于云点确认的云值检索仍是一个具有挑战性的问题。现有的基于深度学习的全球检索任务描述仪通常消耗大量计算资源(如记忆),而这些资源可能不适合有限的硬件资源。在本文中,我们开发了一个高效点云学习网络(EPC-Net),以形成一个全球视觉定位识别描述仪(EPC-Net),这可以取得良好的性能并减少计算记忆和推断时间。首先,我们提议建立一个轻度但有效的神经网络模块(称为 Proxy Conv),以汇总点根线性值云的当地几何参数。我们利用空间邻近矩阵和代理点来简化原始边缘变异以降低记忆消耗量。然后,我们设计了一个轻度的VLAD网络组(G-VLAD),以形成全球检索描述仪。与原始的VLAD网络相比,我们建议一个完全相连的(GFC)层,将高度矢量矢量分解成一个低维矢量的矢量矢量矢量级矢量组,可以减少电子网络的运行中的数据,最终将一个直径数据转换到一个直径流数据,我们的一个矢量数据到一个直径级,从而进一步显示电子矢量数据。