Outlier deletion based improvement on the StOMP algorithm for sparse solution of large-scale underdetermined problems

Achieving convincing visual consistency between virtual objects and a real scene mainly relies on the lighting effects of virtual-real composition scenes. The problem becomes more challenging in lighting virtual objects in a single real image. Recently, scene understanding from a single image has made great progress. The estimated geometry, semantic labels and intrinsic components provide mostly coarse information, and are not accurate enough to re-render the whole scene. However, carefully integrating the estimated coarse information can lead to an estimate of the illumination parameters of the real scene. We present a novel method that uses the coarse information estimated by current scene understanding technology to estimate the parameters of a ray-based illumination model to light virtual objects in a real scene. Our key idea is to estimate the illumination via a sparse set of small 3D surfaces using normal and semantic constraints. The coarse shading image obtained by intrinsic image decomposition is considered as the irradiance of the selected small surfaces. The virtual objects are illuminated by the estimated illumination parameters. Experimental results show that our method can convincingly light virtual objects in a single real image, without any pre-recorded 3D geometry, reflectance, illumination acquisition equipment or imaging information of the image.