Computer vision methods for depth estimation usually use simple camera models with idealized optics. For modern machine learning approaches, this creates an issue when attempting to train deep networks with simulated data, especially for focus-sensitive tasks like Depth-from-Focus. In this work, we investigate the domain gap caused by off-axis aberrations that will affect the decision of the best-focused frame in a focal stack. We then explore bridging this domain gap through aberration-aware training (AAT). Our approach involves a lightweight network that models lens aberrations at different positions and focus distances, which is then integrated into the conventional network training pipeline. We evaluate the generality of pretrained models on both synthetic and real-world data. Our experimental results demonstrate that the proposed AAT scheme can improve depth estimation accuracy without fine-tuning the model or modifying the network architecture.

翻译：用于深度估算的计算机视野方法通常使用带有理想光学的简单相机模型。 对于现代机器学习方法,这在试图用模拟数据来训练深网络时会产生一个问题,特别是针对诸如深度和焦点等重点敏感任务。在这项工作中,我们调查了离轴偏差造成的领域差距,这种差距将影响焦点堆叠中最佳焦点框架的决定。然后我们通过偏差意识培训(AAT)来探索缩小这一领域差距。我们的方法涉及一个轻量级网络,该网络将不同位置和焦点距离的偏差进行透镜分析,然后将其纳入常规网络培训管道。我们评估了合成数据和现实世界数据的预培训模型的一般性。我们的实验结果表明,拟议的AAT计划可以提高深度估算精度,而无需对模型进行微调或修改网络结构。</s>