PointNetGeM: Simple and Efficient Point Cloud Based Network for Place Recognition

Point Cloud based place recognition is a popular area of current research. Most existing methods add additional structures to PointNet to further extract compact information from point clouds, e.g. PointNetVLAD, PCAN. These methods achieved good results but incur severe computational overhead, increase structural complexity and are not easily scalable. In this paper, we propose a simple and efficient method named PointNetGeM. We optimize the training process and achieve discriminative global feature descriptors. Experiments on several datasets demonstrate the effectiveness of our network structure.