  We propose a Point-Voxel DeConvolution (PVDeConv) module for 3D data
autoencoder. To demonstrate its efficiency we learn to synthesize
high-resolution point clouds of 10k points that densely describe the underlying
geometry of Computer Aided Design (CAD) models. Scanning artifacts, such as
protrusions, missing parts, smoothed edges and holes, inevitably appear in real
3D scans of fabricated CAD objects. Learning the original CAD model
construction from a 3D scan requires a ground truth to be available together
with the corresponding 3D scan of an object. To solve the gap, we introduce a
new dedicated dataset, the CC3D, containing 50k+ pairs of CAD models and their
corresponding 3D meshes. This dataset is used to learn a convolutional
autoencoder for point clouds sampled from the pairs of 3D scans - CAD models.
The challenges of this new dataset are demonstrated in comparison with other
generative point cloud sampling models trained on ShapeNet. The CC3D
autoencoder is efficient with respect to memory consumption and training time
as compared to stateof-the-art models for 3D data generation.
