This article investigates the limitations of single image depth prediction (SIDP) under different lighting conditions. Besides that, it also offers a new approach to obtain the ideal condition for SIDP. To satisfy the data requirement, we exploit a photometric stereo dataset consisting of several images of an object under different light properties. In this work, we used a dataset of ancient Roman coins captured under 54 different lighting conditions to illustrate how the approach is affected by them. This dataset emulates many lighting variances with a different state of shading and reflectance common in the natural environment. The ground truth depth data in the dataset was obtained using the stereo photometric method and used as training data. We investigated the capabilities of three different state-of-the-art methods to reconstruct ancient Roman coins with different lighting scenarios. The first investigation compares the performance of a given network using previously trained data to check cross-domains performance. Second, the model is fine-tuned from pre-trained data and trained using 70% of the ancient Roman coin dataset. Both models are tested on the remaining 30% of the data. As evaluation metrics, root mean square error and visual inspection are used. As a result, the methods show different characteristic results based on the lighting condition of the test data. Overall, they perform better at 51° and 71° angles of light, so-called ideal condition afterward. However, they perform worse at 13° and 32° because of the high density of shadows. They also cannot reach the best performance at 82° caused by the reflection that appears on the image. Based on these findings, we propose a new approach to reduce the shadows and reflections on the image using intrinsic image decomposition to achieve a synthetic ideal condition. Based on the results of synthetic images, this approach can enhance the performance of SIDP. For some state-of-the-art methods, it also achieves better results than previous original RGB images.

中文翻译：

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不同光照条件下单幅图像深度预测的研究

本文研究了不同光照条件下单幅图像深度预测 (SIDP) 的局限性。除此之外，它还提供了一种获得SIDP理想条件的新方法。为了满足数据要求，我们利用了一个光度立体数据集，该数据集由一个物体在不同光属性下的多个图像组成。在这项工作中，我们使用了在 54 种不同光照条件下捕获的古罗马硬币数据集来说明该方法如何受到它们的影响。该数据集模拟了自然环境中常见的具有不同阴影状态和反射率的许多光照变化。使用立体光度法获得数据集中的地面实况深度数据并用作训练数据。我们研究了三种不同的最先进方法在不同照明场景下重建古罗马硬币的能力。第一项调查使用先前训练的数据比较给定网络的性能，以检查跨域性能。其次，该模型从预先训练的数据中进行微调，并使用 70% 的古罗马硬币数据集进行训练。两种模型都在剩余 30% 的数据上进行了测试。作为评价指标，使用均方根误差和目视检查。结果，这些方法根据测试数据的光照条件显示出不同的特征结果。总体而言，它们在 51° 和 71° 的光角下表现更好，即所谓的理想条件。然而，由于阴影密度高，它们在 13° 和 32° 处的表现更差。由于图像上出现的反射，它们也无法在 82° 处达到最佳性能。基于这些发现，我们提出了一种新的方法来减少图像上的阴影和反射，使用内在图像分解来实现合成的理想条件。基于合成图像的结果，这种方法可以提高 SIDP 的性能。对于一些最先进的方法，它也取得了比以前的原始 RGB 图像更好的结果。