Coarse- and Fine-Grained Fusion Hierarchical Network for Hole Filling in View Synthesis

Depth image-based rendering (DIBR) techniques play an essential role in free-viewpoint videos (FVVs), which generate the virtual views from a reference 2D texture video and its associated depth information. However, the background regions occluded by the foreground in the reference view will be exposed in the synthesized view, resulting in obvious irregular holes in the synthesized view. To this end, this paper proposes a novel coarse and fine-grained fusion hierarchical network (CFFHNet) for hole filling, which fills the irregular holes produced by view synthesis using the spatial contextual correlations between the visible and hole regions. CFFHNet adopts recurrent calculation to learn the spatial contextual correlation, while the hierarchical structure and attention mechanism are introduced to guide the fine-grained fusion of cross-scale contextual features. To promote texture generation while maintaining fidelity, we equip CFFHNet with a two-stage framework involving an inference sub-network to generate the coarse synthetic result and a refinement sub-network for refinement. Meanwhile, to make the learned hole-filling model better adaptable and robust to the "foreground penetration" distortion, we trained CFFHNet by generating a batch of training samples by adding irregular holes to the foreground and background connection regions of high-quality images. Extensive experiments show the superiority of our CFFHNet over the current state-of-the-art DIBR methods. The source code will be available at https://github.com/wgc-vsfm/view-synthesis-CFFHNet.