Dr. Bokeh: DiffeRentiable Occlusion-aware Bokeh Rendering

Yichen Sheng, Zixun Yu, Lu Ling, Zhiwen Cao, Xuaner Zhang, Xin Lu, Ke Xian, Haiting Lin, Bedrich Benes; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 4515-4525

Bokeh is widely used in photography to draw attention to the subject while effectively isolating distractions in the background. Computational methods can simulate bokeh effects without relying on a physical camera lens but the inaccurate lens modeling in existing filtering-based methods leads to artifacts that need post-processing or learning-based methods to fix. We propose Dr.Bokeh a novel rendering method that addresses the issue by directly correcting the defect that violates the physics in the current filtering-based bokeh rendering equation. Dr.Bokeh first preprocesses the input RGBD to obtain a layered scene representation. Dr.Bokeh then takes the layered representation and user-defined lens parameters to render photo-realistic lens blur based on the novel occlusion-aware bokeh rendering method. Experiments show that the non-learning based renderer Dr.Bokeh outperforms state-of-the-art bokeh rendering algorithms in terms of photo-realism. In addition extensive quantitative and qualitative evaluations show the more accurate lens model further pushes the limit of a closely related field depth-from-defocus.