Learning Non-Lambertian Object Intrinsics Across ShapeNet Categories

We focus on the non-Lambertian object-level intrinsic problem of recovering diffuse albedo, shading, and specular highlights from a single image of an object. Based on existing 3D models in the ShapeNet database, a large-scale object intrinsics database is rendered with HDR environment maps. Millions of synthetic images of objects and their corresponding albedo, shading, and specular ground-truth images are used to train an encoder-decoder CNN, which can decompose an image into the product of albedo and shading components along with an additive specular component. Our CNN delivers accurate and sharp results in this classical inverse problem of computer vision. Evaluated on our realistically synthetic dataset, our method consistently outperforms the state-of-the-art by a large margin. We train and test our CNN across different object categories. Perhaps surprising especially from the CNN classification perspective, our intrinsics CNN generalizes very well across categories. Our analysis shows that feature learning at the encoder stage is more crucial for developing a universal representation across categories. We apply our model to real images and videos from Internet, and observe robust and realistic intrinsics results. Quality non-Lambertian intrinsics could open up many interesting applications such as realistic product search based on material properties and image-based albedo/specular editing.