Neural Cages for Detail-Preserving 3D Deformations

We propose a novel learnable representation for detail-preserving shape deformation extending a traditional cage-based deformation technique. We demonstrate the utility of our method for synthesizing shape variations and deformation transfer.

Differentiable Surface Splatting for Point-based Geometry Processing

We propose a high-fidelity differentiable renderer for point clouds. We demonstrate how the proposed technique can be used to leverage contemporary deep neural networks to achieve state-of-the-art results in challenging geometry processing tasks.

Neural Shapes

Representing and generating shapes using neural networks

Patch-base progressive 3D Point Set Upsampling

We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we …

Point-based geometry processing

Making use of this extremely flexible yet unstructured form of shape represenation.