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Dense point cloud generation from a sparse or incomplete point cloud is acrucial and challenging problem in 3D computer vision and computer graphics. Sofar, the existing methods are either computationally too expensive, suffer fromlimited resolution, or both. In addition, some methods are strictly limited towatertight surfaces -- another major obstacle for a number of applications. Toaddress these issues, we propose a lightweight Convolutional Neural Networkthat learns and predicts the unsigned distance field for arbitrary 3D shapesfor dense point cloud generation using the recently emerged concept of implicitfunction learning. Experiments demonstrate that the proposed architectureoutperforms the state of the art by 87% less model parameters, 36% reducedinference time and improved generated point cloud accuracy.

Convolutional Neural Network-based Efficient Dense Point Cloud Generation using Unsigned Distance Fields