Consistent Bokeh for Multi-View Images With 3D Gaussian Splatting
Rui Huang, Haojie Tao, Liangying Tang, Jingcheng Zeng · IEEE Signal Processing Letters · 2025
Bokeh refocuses the desired regions and generates out-of-focus blur in the remaining regions, which has been well studied for single-image. However, when processing multi-view images of a given scene, the existing Bokeh methods might focus on different objects due to inconsistencies in salient object detection across different views. Additionally, the salient objects with larger depth might be blurred because they are not on the in-focus plane. In this letter, we propose a framework to generate consistent Bokeh across multi-view images. We utilize 3D Gaussian Splatting to render a group of images with a small viewpoint span. To guarantee that the salient objects are identical, we propose aConsistent Saliency Map Generation(CSMG) method with mask tracking. We also propose aDepth Value Reassignment(DVR) method to endow the salient objects with new depth values. With the consistent salient objects and reassigned depth values, we adopt Dr.Bokeh to generate consistent Bokeh effects across multi-view images. Various experiments conducted on the DoF-NeRF dataset demonstrate that our proposed framework outperforms four state-of-the-art bokeh methods on four no-reference image quality evaluation metrics. Code can be found athttps://github.com/jiutaojushi/CBMI.