3D Gaussian Splatting (3DGS) has attracted significant attention for its high-quality novel view rendering, inspiring research to address real-world challenges. While conventional methods depend on sharp images for accurate scene reconstruction, real-world scenarios are often affected by defocus blur due to finite depth of field, making it essential to account for realistic 3D scene representation. In this study, we propose CoCoGaussian, a Circle of Confusion-aware Gaussian Splatting that enables precise 3D scene representation using only defocused images. CoCoGaussian addresses the challenge of defocus blur by modeling the Circle of Confusion (CoC) through a physically grounded approach based on the principles of photographic defocus. Exploiting 3D Gaussians, we compute the CoC diameter from depth and learnable aperture information, generating multiple Gaussians to precisely capture the CoC shape. Furthermore, we introduce a learnable scaling factor to enhance robustness and provide more flexibility in handling unreliable depth in scenes with reflective or refractive surfaces. Experiments on both synthetic and real-world datasets demonstrate that CoCoGaussian achieves state-of-the-art performance across multiple benchmarks.

翻译：三维高斯泼溅（3DGS）因其高质量的新视角渲染能力而备受关注，并激发了针对现实世界挑战的研究。传统方法依赖清晰图像以实现精确的场景重建，然而现实场景常因有限景深而受到离焦模糊的影响，这使得考虑真实的三维场景表示变得至关重要。在本研究中，我们提出了CoCoGaussian，一种弥散圆感知的高斯泼溅方法，能够仅使用离焦图像实现精确的三维场景表示。CoCoGaussian通过基于摄影离焦原理的物理基础方法对弥散圆（CoC）进行建模，从而应对离焦模糊的挑战。利用三维高斯函数，我们从深度和可学习的光圈信息中计算弥散圆直径，并生成多个高斯函数以精确捕捉弥散圆形状。此外，我们引入了一个可学习的缩放因子，以增强鲁棒性，并在处理具有反射或折射表面的场景中不可靠深度时提供更大的灵活性。在合成数据集和真实世界数据集上的实验表明，CoCoGaussian在多个基准测试中均达到了最先进的性能。