Abstract A main challenge for today's renderers is the ever‐growing size of 3D scenes, exceeding the capacity of typically available main memory. This especially holds true for graphics processing units (GPUs) which could otherwise be used to greatly reduce rendering time. A lot of the memory is spent on detailed geometry with mostly imperceptible influence on the final image, even in a global illumination context. Illumination‐driven mesh reduction, a Monte Carlo–based global illumination simulation, steers its mesh reduction towards areas with low visible contribution. While this works well for preserving high‐energy light paths such as caustics, it does have problems: First, objects casting shadows while not being visible themselves are not preserved, resulting in highly inaccurate shadows. Secondly, non‐transparent objects lack proper reduction guidance since there is no importance gradient on their backside, resulting in visible over‐simplification. We present a solution to these problems by extending illumination‐driven mesh reduction with occluder information, focusing on their silhouettes as well as combining it with commonly used error quadrics to preserve geometric features. Additionally, we demonstrate that the combined algorithm still supports iterative refinement of initially reduced geometry, resulting in an image visually similar to an unreduced rendering and enabling out‐of‐core operation.

Abstract Today, Monte Carlo light transport algorithms are used in many applications to render realistic images. Depending on the complexity of the used methods, several light effects can or cannot be found by the sampling process. Especially, specular and smooth glossy surfaces often lead to high noise and missing light effects. Path space regularization provides a solution, improving any sampling algorithm, by modifying the material evaluation code. Previously, Kaplanyan and Dachsbacher [KD13] introduced the concept for pure specular interactions. We extend this idea to the commonly used microfacet models by manipulating the roughness parameter prior to the evaluation. We also show that this kind of regularization requires a change in the MIS weight computation and provide the solution. Finally, we propose two heuristics to adaptively reduce the introduced bias. Using our method, many complex light effects are reproduced and the fidelity of smooth objects is increased. Additionally, if a path was sampleable before, the variance is partially reduced.

Augmented Reality offers many applications today, especially on mobile devices. Due to the lack of mobile hardware for illumination measurements, photorealistic rendering with consistent appearance of virtual objects is still an area of active research. In this paper, we present a full two-stage pipeline for environment acquisition and augmentation of live camera images using a mobile device with a depth sensor. We show how to directly work on a recorded 3D point cloud of the real environment containing high dynamic range color values. For unknown and automatically changing camera settings, a color compensation method is introduced. Based on this, we show photorealistic augmentations using variants of differential light simulation techniques. The presented methods are tailored for mobile devices and run at interactive frame rates. However, our methods are scalable to trade performance for quality and can produce quality renderings on desktop hardware.

In this paper we present a new real-time approach for indirect global illumination under dynamic lighting conditions. We use surfels to gather a sampling of the local illumination and propagate the light through the scene using a hierarchy and a set of precomputed light transport paths. The light is then aggregated into caches for lighting of static and dynamic geometry. By using a spherical harmonics representation, caches preserve incident light directions to allow both diffuse and slightly glossy BRDFs for indirect lighting.