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Tremendous progress in the development and accessibility of high dynamic range (HDR) technology that has happened just recently results in fast proliferation of HDR synthetic image sequences and captured HDR video. When properly processed, such HDR data can lead to very convincing and realistic results even when presented on traditional low dynamic range (LDR) display devices. This requires real-time local contrast compression (tone mapping) with simultaneous modeling of important in HDR image perception effects such as visual acuity, glare, day and night vision. We propose a unified model to include all those effects into a common computational framework, which enables an efficient implementation on currently available graphics hardware. We develop a post processing module which can be added as the final stage of any real-time rendering system, game engine, or digital video player, which enhances the realism and believability of displayed image streams.

Global illumination dramatically improves realistic appearance of rendered scenes, but usually it is neglected in VR systems due to its high costs. In this work we present an efficient global illumination solution specifically tailored for those CAVE applications, which require an immediate response for dynamic light changes and allow for free motion of the observer, but involve scenes with static geometry. As an application example we choose the car interior modeling under free driving conditions. We illuminate the car using dynamically changing High Dynamic Range (HDR) environment maps and use the Precomputed Radiance Transfer (PRT) method for the global illumination computation. We leverage the PRT method to handle scenes with non-trivial topology represented by complex meshes. Also, we propose a hybrid of PRT and final gathering approach for high-quality rendering of objects with complex Bi-directional Reflectance Distribution Function (BRDF). We use this method for predictive rendering of the navigation LCD panel based on its measured BRDF. Since the global illumination computation leads to HDR images we propose a tone mapping algorithm tailored specifically for the CAVE. We employ head tracking to identify the observed screen region and derive for it proper luminance adaptation conditions, which are then used for tone mapping on all walls in the CAVE. We distribute our global illumination and tone mapping computation on all CPUs and CPUs available in the CAVE, which enables us to achieve interactive performance even for the costly final gathering approach.