  Semantic shape completion is a challenging problem in 3D computer vision
where the task is to generate a complete 3D shape using a partial 3D shape as
input. We propose a learning-based approach to complete incomplete 3D shapes
through generative modeling and latent manifold optimization. Our algorithm
works directly on point clouds. We use an autoencoder and a GAN to learn a
distribution of embeddings for point clouds of object classes. An input point
cloud with missing regions is first encoded to a feature vector. The
representations learnt by the GAN are then used to find the best latent vector
on the manifold using a combined optimization that finds a vector in the
manifold of plausible vectors that is close to the original input (both in the
feature space and the output space of the decoder). Experiments show that our
algorithm is capable of successfully reconstructing point clouds with large
missing regions with very high fidelity without having to rely on exemplar
based database retrieval.
