3D Object Super-Resolution

We consider the problem of scaling deep generative shape models to high-resolution. To this end, we introduce a novel method for the fast up-sampling of 3D objects in voxel space by super-resolution on the six orthographic depth projections. We demonstrate the training of object-specific super-resolution CNNs for depth maps and silhouettes. This allows us to efficiently generate high-resolution objects, without the cubic computational costs associated with voxel data. We evaluate our work on multiple experiments concerning high-resolution 3D objects, and show our system is capable of accurately increasing the resolution of voxelized objects by a factor of up to 16, to produce objects at resolutions as large as 512$\mathbf{\times}$512$\mathbf{\times}$512 from 32$\mathbf{\times}$32$\mathbf{\times}$32 resolution inputs. Additionally, we demonstrate our method can be easily applied in conjunction with the reconstruction of high-resolution objects from RGB images to achieve quantitative and qualitative state-of-the-art performance for this task.