  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.
