Decomposing a scene into its shape, reflectance, and illumination is a challenging but
important problem in computer vision and graphics. This problem is inherently more …

Our ability to train end-to-end systems for 3D human pose estimation from single images is
currently constrained by the limited availability of 3D annotations for natural images. Most …

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 an automatic method to infer high dynamic range illumination from a single,
limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to …

This paper studies single-image depth perception in the wild, ie, recovering depth from a
single image taken in unconstrained settings. We introduce a new dataset “Depth in the …

Several RGB-D datasets have been publicized over the past few years for facilitating
research in computer vision and robotics. However, the lack of comprehensive and fine …

We present a CNN-based technique to estimate high-dynamic range outdoor illumination
from a single low dynamic range image. To train the CNN, we leverage a large dataset of …

Monocular 3D human-pose estimation from static images is a challenging problem, due to
the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D. In this paper, we …

This paper follows the idea of divide and conquer to propose a multi-exposure fusion
network for the unsupervised generation of high dynamic range-like images. We develop a …

Intrinsic image decomposition is a long-standing, highly challenging computer vision
problem, where ground truth data is very difficult to acquire. We explore the idea of using …