VC-GS: View-Consistent Deblurring Gaussian Splatting Via Alternating Branch Optimization

Recent studies on 3D Gaussian splatting demonstrate promising applications in novel view synthesis using several posed images. These images captured in real-life scenarios may be blurry and lead to unsatisfactory synthesis quality. Utilizing 2D image deblurring models to sharpen the blurry image before 3D Gaussian splatting is a natural inspiration. However, directly applying 2D image deblurring models for novel view synthesis results in poor performance due to the inevitable inconsistencies. For the above problem, this paper introduces a new approach called view-consistent Gaussian splatting via alternating branch optimization strategy(VC-GS). VC-GS consists of two branches: preceding branch and subsequent branch. The preceding branch restores multi-view blurry inputs using a 2D deblurring model before 3D Gaussian splatting, while the subsequent branch directly applies 3D Gaussian splatting to the blurry inputs and restores the synthesized views with the same model. Through the alternating branch optimization of the preceding branch and subsequent branch, the 3D Gaussians in the preceding branch can achieve high-quality reconstruction and maintain view consistency under blurry inputs. To validate the effectiveness of the approach, we conducted qualitative and quantitative experiments on synthetic and real datasets, demonstrating that the proposed approach achieves better results. Our project page is available at https://github.com/DTG777/View-Consistent-Gaussian-Splatting-from-Blurry-Images-for-Novel-View-Synthesis .