Data-driven Upsampling of Point Clouds
This addresses the need for high-quality point cloud upsampling in applications like reconstruction and rendering, but it appears incremental as it builds on existing deep learning approaches with specific improvements.
The paper tackles the problem of upsampling sparse 3D point clouds for geometric operations by proposing a data-driven deep learning algorithm that learns latent features without hard-coded rules, resulting in more uniform and accurate upsamplings compared to a baseline optimization-based method.
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.