Multimodal Shape Completion via Conditional Generative Adversarial Networks
This addresses the limitation of single-output methods in shape completion for applications like 3D modeling, though it is incremental as it builds on existing generative models.
The paper tackles the problem of multimodal shape completion, where partial shapes are completed with multiple plausible outputs to address ambiguity in missing geometry, achieving diversity and quality in results.
Several deep learning methods have been proposed for completing partial data from shape acquisition setups, i.e., filling the regions that were missing in the shape. These methods, however, only complete the partial shape with a single output, ignoring the ambiguity when reasoning the missing geometry. Hence, we pose a multi-modal shape completion problem, in which we seek to complete the partial shape with multiple outputs by learning a one-to-many mapping. We develop the first multimodal shape completion method that completes the partial shape via conditional generative modeling, without requiring paired training data. Our approach distills the ambiguity by conditioning the completion on a learned multimodal distribution of possible results. We extensively evaluate the approach on several datasets that contain varying forms of shape incompleteness, and compare among several baseline methods and variants of our methods qualitatively and quantitatively, demonstrating the merit of our method in completing partial shapes with both diversity and quality.