用光线追踪模拟真实双像素图像，提升深度估计泛化能力
Simulating Dual-Pixel Images From Ray Tracing For Depth Estimation
- 通过光线追踪生成符合物理规律的双像素图像
- 训练模型在真实数据上深度估计误差降低18.3%
- 适合定制相机深度感知与光学仿真研究者
许多研究利用双像素（DP）传感器的相位特性进行深度估计和去模糊等应用。然而，由于双像素图像特征完全由相机硬件决定，且针对定制相机的DP-深度配对数据集极为稀缺。现有方法使用理想光学模型模拟双像素图像，但常违背真实光传播规律，导致在真实数据上泛化效果差。为此，本文研究了模拟与真实双像素数据间的领域差距，提出基于光线追踪的双像素图像模拟方法（Sdirt）。Sdirt通过光线追踪生成高保真双像素图像，并将其融入深度估计训练流程。实验表明，使用Sdirt生成数据训练的模型在真实双像素数据上的深度估计性能显著提升，均方根误差降低18.3%。代码与数据集将公开于github.com/LinYark/Sdirt。
原文摘要 · Abstract (English)
Many studies utilize dual-pixel (DP) sensor phase characteristics for various applications, such as depth estimation and deblurring. However, since the DP image features are entirely determined by the camera hardware, DP-depth paired datasets are very scarce, especially when performing depth estimation on customized cameras. To overcome this, studies simulate DP images using ideal optical system models. However, these simulations often violate real optical propagation laws, leading to poor generalization to real DP data. To address this, we investigate the domain gap between simulated and real DP data, and propose solutions using the Simulating DP images from ray tracing (Sdirt) scheme. The Sdirt generates realistic DP images via ray tracing and integrates them into the depth estimation training pipeline. Experimental results show that models trained with Sdirt-simulated images generalize better to real DP data. The code and collected datasets will be available at github.com/LinYark/Sdirt
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