让3D高斯点云支持可调景深，真实还原镜头虚化效果。
DoF-Gaussian: Controllable Depth-of-Field for 3D Gaussian Splatting
- 基于光学原理构建镜头成像模型，实现景深可控。
- 引入场景自适应深度先验与模糊到清晰的适配机制。
- 适用于交互式应用，可生成带真实虚化的3D视图。
3D高斯点云（3D-GS）在实时重建3D场景和生成高质量新视角方面取得显著进展，但其假设输入图像基于针孔成像且全焦，限制了在真实世界浅景深图像中的应用。本文提出DoF-Gaussian，一种支持可调景深的3D-GS方法。我们基于几何光学构建基于镜头的成像模型，以控制景深效果。为保证场景几何精度，引入按场景调整的深度先验，并采用模糊到清晰的适配策略，缩小弥散圆差异。此外，我们构建了一个合成数据集，用于评估重聚焦能力及模型学习精确镜头参数的能力。框架可定制，支持多种交互式应用。大量实验验证了方法的有效性。项目地址：https://dof-gaussian.github.io。
原文摘要 · Abstract (English)
Recent advances in 3D Gaussian Splatting (3D-GS) have shown remarkable success in representing 3D scenes and generating high-quality, novel views in real-time. However, 3D-GS and its variants assume that input images are captured based on pinhole imaging and are fully in focus. This assumption limits their applicability, as real-world images often feature shallow depth-of-field (DoF). In this paper, we introduce DoF-Gaussian, a controllable depth-of-field method for 3D-GS. We develop a lens-based imaging model based on geometric optics principles to control DoF effects. To ensure accurate scene geometry, we incorporate depth priors adjusted per scene, and we apply defocus-to-focus adaptation to minimize the gap in the circle of confusion. We also introduce a synthetic dataset to assess refocusing capabilities and the model's ability to learn precise lens parameters. Our framework is customizable and supports various interactive applications. Extensive experiments confirm the effectiveness of our method. Our project is available at https://dof-gaussian.github.io.
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