Reflection mapping

Abstract: We consider the rendering of diffuse objects under distant illumination, as specified by an environment map. Using an analytic expression for the irradiance in terms of spherical harmonic coefficients of the lighting, we show that one needs to compute and use only 9 coefficients, corresponding to the lowest-frequency modes of the illumination, in order to achieve average errors of only 1%. In other words, the irradiance is insensitive to high frequencies in the lighting, and is well approximated using only 9 parameters. In fact, we show that the irradiance can be procedurally represented simply as a quadratic polynomial in the cartesian components of the surface normal, and give explicit formulae. These observations lead to a simple and efficient procedural rendering algorithm amenable to hardware implementation, a prefiltering method up to three orders of magnitude faster than previous techniques, and new representations for lighting design and image-based rendering.

Abstract: 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 outdoor panoramas. We fit a low-dimensional physically-based outdoor illumination model to the skies in these panoramas giving us a compact set of parameters (including sun position, atmospheric conditions, and camera parameters). We extract limited field-of-view images from the panoramas, and train a CNN with this large set of input image–output lighting parameter pairs. Given a test image, this network can be used to infer illumination parameters that can, in turn, be used to reconstruct an outdoor illumination environment map. We demonstrate that our approach allows the recovery of plausible illumination conditions and enables photorealistic virtual object insertion from a single image. An extensive evaluation on both the panorama dataset and captured HDR environment maps shows that our technique significantly outperforms previous solutions to this problem.

Abstract: The solution m the Skorokhoci Problem defines a deieiminisiic mapping of paths that has been found to be useful in several areas of application. Typical uses of the mapping are construction and analysis of deterministic and stochastic processes that are constrained to remain in a given fixed set, such as stochastic differential equations with reflection and stochastic approximation schemes for problems with constraints In this paper we focus on the case where the set is a convex polyhedron and where the directions along which the constraint mechanism is applied arc possibly oblique and multivalued at corner points. Our goal is to characterize as completely as possible those situations in which the solution mapping is Lipschitz continuous. Our approach is geometric in nature, and shows that the Lipschitz continuity holds when a certain convex set, defined in terms of the normal directions to the faces of the polyhedron and the directions of the constraint mechanism, can be shown to exist. All previous inst...

Abstract: This paper introduces a new process, environment matting, which captures not just a foreground object and its traditional opacity matte from a real-world scene, but also a description of how that object refracts and reflects light, which we call an environment matte The foreground object can then be placed in a new environment, using environment compositing, where it will refract and reflect light from that scene Objects captured in this way exhibit not only specular but glossy and translucent effects, as well as selective attenuation and scattering of light according to wavelength Moreover, the environment compositing process, which can be performed largely with texture mapping operations, is fast enough to run at interactive speeds on a desktop PC We compare our results to photos of the same objects in real scenes Applications of this work include the relighting of objects for virtual and augmented reality, more realistic 3D clip art, and interactive lighting design CR Categories: I210 [Artificial Intelligence]: Vision and Scene Understanding – modeling and recovery of physical attributes; I33 [Computer Graphics]: Picture/Image Generation – display algorithms; I37 [Computer Graphics]: ThreeDimensional Graphics and Realism – color, shading, shadowing, and texture Additional