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【ECCV 2020 Spotlight】Multimodal Shape Completion via Conditional Generative Adversarial Networks

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.

 

 

Paper: https://arxiv.org/abs/2003.07717
Project page: https://chriswu1997.github.io/files/multimodal-pc/index.html
Code: https://github.com/ChrisWu1997/Multimodal-Shape-Completion

 

 

European Conference on Computer Vision (ECCV) is the top European conference in the image analysis area. ECCV, along with CVPR and ICCV, are regarded as the top conferences in the field of computer vision. ECCV is held biennially. Due to concerns about COVID-19, ECCV 2020 will be hosted online from August 23rd to August 28th, 2020.