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Semantic shape completion is a challenging problem in 3D computer visionwhere the task is to generate a complete 3D shape using a partial 3D shape asinput. We propose a learning-based approach to complete incomplete 3D shapesthrough generative modeling and latent manifold optimization. Our algorithmworks directly on point clouds. We use an autoencoder and a GAN to learn adistribution of embeddings for point clouds of object classes. An input pointcloud with missing regions is first encoded to a feature vector. Therepresentations learnt by the GAN are then used to find the best latent vectoron the manifold using a combined optimization that finds a vector in themanifold of plausible vectors that is close to the original input (both in thefeature space and the output space of the decoder). Experiments show that ouralgorithm is capable of successfully reconstructing point clouds with largemissing regions with very high fidelity without having to rely on exemplarbased database retrieval.

High Fidelity Semantic Shape Completion for Point Clouds using Latent Optimization