DTGS: Defocus‐Tolerant View Synthesis Using Gaussian Splatting - Dai, Xinying; Yao, Li (Computer animation and virtual worlds, 2025)
ABSTRACT Defocus blur poses a significant challenge for 3D reconstruction, as traditional methods often struggle to maintain detail and accuracy in blurred regions. Building upon the recent advancements in the 3DGS technique, we propose an architecture for 3D scene reconstruction from defocused blurry images. Due to the sparsity of point clouds initialized by SfM, we improve the scene representation by reasonably filling in new Gaussians where the Gaussian field is insufficient. During the optimization phase, we adjust the gradient field based on the depth values of the Gaussians and introduce perceptual loss in the objective function to reduce reconstruction bias caused by blurriness and enhance the realism of the rendered results. Experimental results on both synthetic and real datasets show that our method outperforms existing approaches in terms of reconstruction quality and robustness, even under challenging defocus blur conditions. This paper presents an architecture for 3D scene reconstruction from defocused blurry images. Due to the sparsity of point clouds initialized by SfM, we improve the scene representation by reasonably filling in new Gaussians where the Gaussian field is insufficient. During the optimization phase, we adjust the gradient field based on the depth values of the Gaussians and introduce perceptual loss in the objective function to reduce reconstruction bias caused by blurriness and enhance the realism of the rendered results.