  High quality upsampling of sparse 3D point clouds is critically useful for a
wide range of geometric operations such as reconstruction, rendering, meshing,
and analysis. In this paper, we propose a data-driven algorithm that enables an
upsampling of 3D point clouds without the need for hard-coded rules. Our
approach uses a deep network with Chamfer distance as the loss function,
capable of learning the latent features in point clouds belonging to different
object categories. We evaluate our algorithm across different amplification
factors, with upsampling learned and performed on objects belonging to the same
category as well as different categories. We also explore the desirable
characteristics of input point clouds as a function of the distribution of the
point samples. Finally, we demonstrate the performance of our algorithm in
single-category training versus multi-category training scenarios. The final
proposed model is compared against a baseline, optimization-based upsampling
method. Results indicate that our algorithm is capable of generating more
uniform and accurate upsamplings.
