ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

Abstract

In general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than albedo changes, these methods may fail in distinguishing strong (cast) shadows from albedo variations. That in return may leak into albedo map predictions. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows). The aim is to distinguish strong cast shadows from reflectance variations. Two end-to-end supervised CNN models (ShadingNets) are proposed exploiting the fine-grained shading model. Furthermore, surface normal features are jointly learned by the proposed CNN networks. Surface normals are expected to assist the decomposition task. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with intrinsic image ground-truths. Large scale experiments show that our CNN approach using fine-grained shading decomposition outperforms state-of-the-art methods using unified shading.

Invariant Descriptors for Intrinsic Reflectance Optimization

“…Lettry et al investigate adversarial learning [37]. Baslamisli et al propose fine-grained shading decomposition [38]. We refer readers to the work of Sial et al that provides a comprehensive overview of the deep learning based methods and large-scale datasets [39].…”