This paper addresses the task of estimating the light arriving from all directions to a 3D point
observed at a selected pixel in an RGB image. This task is challenging because it requires …

We propose a deep inverse rendering framework for indoor scenes. From a single RGB
image of an arbitrary indoor scene, we obtain a complete scene reconstruction, estimating …

Reconstructing shape and reflectance properties from images is a highly under-constrained
problem, and has previously been addressed by using specialized hardware to capture …

We present an image-based relighting method that can synthesize scene appearance under
novel, distant illumination from the visible hemisphere, from only five images captured under …

We present a method to estimate lighting from a single image of an indoor scene. Previous
work has used an environment map representation that does not account for the localized …

We propose a real-time method to estimate spatially-varying indoor lighting from a single
RGB image. Given an image and a 2D location in that image, our CNN estimates a 5th order …

We propose a data-driven learned sky model, which we use for outdoor lighting estimation
from a single image. As no large-scale dataset of images and their corresponding ground …

Reflections on glossy objects contain valuable and hidden information about the
surrounding environment. By converting these objects into cameras, we can unlock exciting …

We present the first end-to-end approach for real-time material estimation for general object
shapes with uniform material that only requires a single color image as input. In addition to …

Human modeling and relighting are two fundamental problems in computer vision and
graphics, where high-quality datasets can largely facilitate related research. However, most …