基于3D高斯溅射孪生散焦的深度估计
Depth Estimation Based on 3D Gaussian Splatting Siamese Defocus
AI总结：
针对散焦深度估计依赖难以获取的全对焦图像问题，提出基于3D高斯溅射和孪生网络的自监督框架，从单张散焦图像预测散焦图和CoC并用于深度估计，在合成与真实数据集上验证了有效性。
摘要：
深度估计是3D几何中的基础任务。立体深度估计可通过三角测量方法实现，但单目方法需整合全局与局部信息，难度更大。散焦深度（DFD）方法利用相机镜头模型和参数从模糊图像中恢复深度信息，已被证明性能良好。然而这些方法依赖全对焦（AIF）图像进行深度估计，这在实际应用中几乎无法获取。为此，我们提出一种基于3D高斯溅射和孪生网络的自监督框架。通过学习焦栈中同一场景不同焦距下的模糊程度，该框架从单张散焦图像预测散焦图和弥散圆（CoC），并将散焦图输入DepthNet进行单目深度估计。3D高斯溅射模型利用预测的CoC渲染散焦图像，其与真实散焦图像的差异为孪生散焦自监督网络提供额外监督信号。该框架已在人工合成和真实模糊数据集上验证，后续定量和可视化实验表明其作为DFD方法非常有效。
英文摘要：
Depth estimation is a fundamental task in 3D geometry. While stereo depth estimation can be achieved through triangulation methods, it is not as straightforward for monocular methods, which require the integration of global and local information. The Depth from Defocus (DFD) method utilizes camera lens models and parameters to recover depth information from blurred images and has been proven to perform well. However, these methods rely on All-In-Focus (AIF) images for depth estimation, which is nearly impossible to obtain in real-world applications. To address this issue, we propose a self-supervised framework based on 3D Gaussian splatting and Siamese networks. By learning the blur levels at different focal distances of the same scene in the focal stack, the framework predicts the defocus map and Circle of Confusion (CoC) from a single defocused image, using the defocus map as input to DepthNet for monocular depth estimation. The 3D Gaussian splatting model renders defocused images using the predicted CoC, and the differences between these and the real defocused images provide additional supervision signals for the Siamese Defocus self-supervised network. This framework has been validated on both artificially synthesized and real blurred datasets. Subsequent quantitative and visualization experiments demonstrate that our proposed framework is highly effective as a DFD method.
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