PVLNet: Parameterized-View-Learning neural network for 3D shape recognition

Hongbin Xu, Lvequan Wang, Qiuxia Wu, Wenxiong Kang

Abstract: Highlights•We propose parameterized-view-learning mechanism which aims to parameterize the views as parameters in the multi-view networks.•We provide an intuitionistic interpretation of parameterized-view-learning mechanism by analyzing point cloud networks as multi-view networks.•We propose a novel efficient multi-view network based on parameterized-view-learning mechanism, called PVLNet.•Our model can achieve state-of-the-art performance with 1/10 parameters compared with previous multi-view networks.•The experimental results on synthetic and real-world 3D datasets demonstrate the effective performance of the proposed PVLNet.