Large-scale place recognition is a fundamental but challenging task, which plays an increasingly important role in autonomous driving and robotics. Existing methods have achieved acceptable good performance, however, most of them are concentrating on designing elaborate global descriptor learning network structures. The importance of feature generalization and descriptor post-enhancing has long been neglected. In this work, we propose a novel method named GIDP to learn a Good Initialization and Inducing Descriptor Poseenhancing for Large-scale Place Recognition. In particular, an unsupervised momentum contrast point cloud pretraining module and a reranking-based descriptor post-enhancing module are proposed respectively in GIDP. The former aims at learning a good initialization for the point cloud encoding network before training the place recognition model, while the later aims at post-enhancing the predicted global descriptor through reranking at inference time. Extensive experiments on both indoor and outdoor datasets demonstrate that our method can achieve state-of-the-art performance using simple and general point cloud encoding backbones.
翻译:大规模地点识别是一项基本但具有挑战性的任务,在自主驾驶和机器人方面发挥着越来越重要的作用。现有的方法已经取得了可接受的良好业绩。但是,大多数方法都集中在设计周密的全球描述性学习网络结构上。特征一般化和描述性说明后增强的重要性长期以来一直被忽视。在这项工作中,我们提出了名为GIDP的新颖方法,以学习良好的初始化和引导描述性说明性说明增强大规模地点识别。特别是,在GIDP中,分别提出了一个未受监督的动力对比点云预培训模块和一个基于分级的描述性说明性后增强模块。前者的目的是在培训地点识别模型之前学习点云编码网络的良好初始化,而后来的目标是通过在推论时间的顺序上重新排序来提升预测的全球描述性说明性说明。关于室内和室外数据集的广泛实验表明,我们的方法可以使用简单和一般的点云编码骨干实现最先进的状态性表现。