1. A method for automatically estimating illumination sources associated with a digital image depicting a scene, the method comprising: detecting light sources depicted in the digital image that meet a given thresholding requirement, the digital image comprising a plurality of pixels; estimating a dense depth associated with the digital image by estimating a focal length of a camera used to capture the digital image and a sparse surface orientation map based on geometric constraints depicted in the digital image, applying a non-parametric depth sampling approach that uses a dataset of RGB-D images to estimate a depth at every pixel of the digital image that is consistent with the sparse surface orientation map, and providing a set of estimated depths and normals that represent a geometry model of the scene, wherein geometric cues provided by the sparse surface orientation map are used in estimating the dense depth to enforce orientation constraints, piecewise-planarity, and surface smoothness; pruning the detected light sources using the estimated dense depth; and estimating light outside of a view frustum associated with the digital image using a dataset of image-based lights comprising spherical high dynamic range images, wherein each spherical image is sub-sampled into rectilinear projections and matched to the digital image, and wherein one or more top matching candidate image-based lights are used as distinct sources of light.