MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects

Abstract

Partial occlusion effects are a phenomenon that blurry objects near a camera are semi-transparent, resulting in partial appearance of occluded background. However, it is challenging for existing bokeh rendering methods to simulate realistic partial occlusion effects due to the missing information of the occluded area in an all-in-focus image. Inspired by the learnable 3D scene representation, Multiplane Image (MPI), we attempt to address the partial occlusion by introducing a novel MPI-based high-resolution bokeh rendering framework, termed MPIB. To this end, we first present an analysis on how to apply the MPI representation to bokeh rendering. Based on this analysis, we propose an MPI representation module combined with a background inpainting module to implement high-resolution scene representation. This representation can then be reused to render various bokeh effects according to the controlling parameters. To train and test our model, we also design a ray-tracing-based bokeh generator for data generation. Extensive experiments on synthesized and real-world images validate the effectiveness and flexibility of this framework.

Cite

Text

Peng et al. "MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-20068-7_34

Markdown

[Peng et al. "MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/peng2022eccv-mpib/) doi:10.1007/978-3-031-20068-7_34

BibTeX

@inproceedings{peng2022eccv-mpib,
  title     = {{MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects}},
  author    = {Peng, Juewen and Zhang, Jianming and Luo, Xianrui and Lu, Hao and Xian, Ke and Cao, Zhiguo},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2022},
  doi       = {10.1007/978-3-031-20068-7_34},
  url       = {https://mlanthology.org/eccv/2022/peng2022eccv-mpib/}
}