We investigate the use of photometric invariance and deep learning to compute intrinsic images (albedo and shading). We propose albedo and shading gradient descriptors which are derived from physics-based models. Using the descriptors, albedo transitions are masked out and an initial sparse shading map is calculated directly from the corresponding $RGB$ image gradients in a learning-free unsupervised manner. Then, an optimization method is proposed to reconstruct the full dense shading map. Finally, we integrate the generated shading map into a novel deep learning framework to refine it and also to predict corresponding albedo image to achieve intrinsic image decomposition. By doing so, we are the first to directly address the texture and intensity ambiguity problems of the shading estimations. Large scale experiments show that our approach steered by physics-based invariant descriptors achieve superior results on MIT Intrinsics, NIR-RGB Intrinsics, Multi-Illuminant Intrinsic Images, Spectral Intrinsic Images, As Realistic As Possible, and competitive results on Intrinsic Images in the Wild datasets while achieving state-of-the-art shading estimations.

我们研究了使用光度不变性和深度学习来计算内在图像（反照率和阴影）。我们提出反照率和阴影梯度描述符，它们是基于物理模型的。使用描述符，将反照率转换屏蔽掉，并直接从对应的阴影中直接计算出初始稀疏阴影贴图$\mathrm{[R}G乙$以无学习的无监督方式进行图像渐变。然后，提出了一种优化方法来重建全密阴影图。最后，我们将生成的阴影贴图集成到新颖的深度学习框架中进行优化，并预测相应的反照率图像以实现固有图像分解。通过这样做，我们是第一个直接解决阴影估计的纹理和强度模糊性问题的人。大规模实验表明，由基于物理的不变描述符指导的方法在MIT固有，NIR-RGB固有，多光源固有图像，光谱固有图像，尽可能真实的情况下取得了优异的结果，并且在野外固有图像上也具有竞争性结果数据集，同时获得最新的阴影估计。