基于语义关系的单视图隐式场三维重建技术

With the development of deep learning techniques, learning implicit field for 3D shape reconstruction has become a heated topic, because implicit field can help networks learn a reasonable and sophisticated reconstruction model than explicit methods. However, there are still some challenges to be solved including lacking semantic information, local detail incompletions and so on. Thus, rather than recreating the entire model from a decoder directly, we first reconstruct the semantic components of a single model using an implicit filed structure based on semantic sections in our paper. Then we aggregate the reconstructed semantic parts together to get the final model. Finally, we test those results on the public 3D shape dataset PartNet and compare them to other cutting-edge single-view reconstruction approaches. It’s obvious that using a semantic part-based implicit field can learn more reasonable shape representations for reconstruction.