  Shape completion is the problem of completing partial input shapes such as
partial scans. This problem finds important applications in computer vision and
robotics due to issues such as occlusion or sparsity in real-world data.
However, most of the existing research related to shape completion has been
focused on completing shapes by learning a one-to-one mapping which limits the
diversity and creativity of the produced results. We propose a novel multimodal
shape completion technique that is effectively able to learn a one-to-many
mapping and generates diverse complete shapes. Our approach is based on the
conditional Implicit MaximumLikelihood Estimation (IMLE) technique wherein we
condition our inputs on partial 3D point clouds. We extensively evaluate our
approach by comparing it to various baselines both quantitatively and
qualitatively. We show that our method is superior to alternatives in terms of
completeness and diversity of shapes.
