Abstract Realistically inserting virtual 3D objects into real‐world images requires perceptually coherent shadowing of the object and background scene. Achieving this in single‐view indoor scenes with sunlight is challenging due to complex, partially visible occluders and indirect lighting. Environment maps alone cannot produce realistic shadows on virtual objects, and any representation (scene parameters) used for rendering must be practically estimable. We introduce ProjectiveShading, the first automatic method for inverse‐ and re‐rendering that handles bi‐directional shadow interactions for realistic object composition. Our key innovation is the sunlight map, a 2D image encoding direct sunlight and arbitrary occlusions. It is generated from single‐view estimations using off‐the‐shelf models and is compatible with standard rendering engines. We also propose algorithms to estimate sunlight direction and to blend virtual and real shadows while preserving background textures. Experiments on synthetic and in‐the‐wild images show our method outperforms previous approaches.

We present CRefNet, a hybrid transformer-convolutional deep neural network for consistent reflectance estimation in intrinsic image decomposition. Estimating consistent reflectance is particularly challenging when the same material appears differently due to changes in illumination. Our method achieves enhanced global reflectance consistency via a novel transformer module that converts image features to reflectance features. At the same time, this module also exploits long-range data interactions. We introduce reflectance reconstruction as a novel auxiliary task that shares a common decoder with the reflectance estimation task, and which substantially improves the quality of reconstructed reflectance maps. Finally, we improve local reflectance consistency via a new rectified gradient filter that effectively suppresses small variations in predictions without any overhead at inference time. Our experiments show that our contributions enable CRefNet to predict highly consistent reflectance maps and to outperform the state of the art by 10% WHDR.

Intrinsic image decomposition, i.e., decomposing a natural image into a reflectance image and a shading image, is used in many augmented reality applications for achieving better visual coherence between virtual contents and real scenes. The main challenge is that the decomposition is ill-posed, especially in indoor scenes where lighting conditions are complicated, while real training data is inadequate. To solve this challenge, we propose NIID-Net, a novel learning-based framework that adapts surface normal knowledge for improving the decomposition. The knowledge learned from relatively more abundant data for surface normal estimation is integrated into intrinsic image decomposition in two novel ways. First, normal feature adapters are proposed to incorporate scene geometry features when decomposing the image. Secondly, a map of integrated lighting is proposed for propagating object contour and planarity information during shading rendering. Furthermore, this map is capable of representing spatially-varying lighting conditions indoors. Experiments show that NIID-Net achieves competitive performance in reflectance estimation and outperforms all previous methods in shading estimation quantitatively and qualitatively. The source code of our implementation is released at https://github.com/zju3dv/NIID-Net.