Multimodal Shape Completion via Conditional Generative Adversarial Networks
arXiv:2003.07717
Abstract
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.
arXiv commentAccepted to ECCV 2020 (spotlight). Project page at https://chriswu1997.github.io/files/multimodal-pc/index.html
References in corpus (8)
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- Toward Multimodal Image-to-Image Translation
- Learning Representations and Generative Models for 3D Point Clouds
- PCPNET: Learning Local Shape Properties from Raw Point Clouds
- Dense 3D Object Reconstruction from a Single Depth View
- Unpaired Point Cloud Completion on Real Scans using Adversarial Training
- Visual Object Networks: Image Generation with Disentangled 3D Representation
- PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes