In this paper, we propose Neural Points, a novel point cloud representation
and apply it to the arbitrary-factored upsampling task. Different from
traditional point cloud representation where each point only represents a
position or a local plane in the 3D space, each point in Neural Points
represents a local continuous geometric shape via neural fields. Therefore,
Neural Points contain more shape information and thus have a stronger
representation ability. Neural Points is trained with surface containing rich
geometric details, such that the trained model has enough expression ability
for various shapes. Specifically, we extract deep local features on the points
and construct neural fields through the local isomorphism between the 2D
parametric domain and the 3D local patch. In the final, local neural fields are
integrated together to form the global surface. Experimental results show that
Neural Points has powerful representation ability and demonstrate excellent
robustness and generalization ability. With Neural Points, we can resample
point cloud with arbitrary resolutions, and it outperforms the state-of-the-art
point cloud upsampling methods. Code is available at
this https URL

本文提出了一种新的点云表示 Neural Points，并将其应用于任意分解上采样任务中。相较于传统的点云表示，每个点在 Neural Points 中通过神经场表示局部连续几何形状，因此 Neural Points 包含更多的形状信息并具有更强的表征能力。实验证明，Neural Points 在任意分辨率上对点云进行重新采样的表现优于最先进的点云上采样方法。