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A CAVE System for Interactive Modeling of Global Illumination in Car Interior

MPS-Authors

Dmitriev,  Kirill
Max Planck Society;

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Annen,  Thomas
Computer Graphics, MPI for Informatics, Max Planck Society;

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Krawczyk,  Grzegorz
Computer Graphics, MPI for Informatics, Max Planck Society;

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Myszkowski,  Karol
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons45449

Seidel,  Hans-Peter       
Computer Graphics, MPI for Informatics, Max Planck Society;

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引用

Dmitriev, K., Annen, T., Krawczyk, G., Myszkowski, K., & Seidel, H.-P. (2004). A CAVE System for Interactive Modeling of Global Illumination in Car Interior. In R., Lau, & G., Baciu (Eds.), Proceedings of the ACM Symposium on Virtual Reality Software and Technology (pp. 137-145). New York, USA: ACM.


引用: https://hdl.handle.net/11858/00-001M-0000-000F-29FF-A
要旨
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 GPUs available in the
CAVE, which enables us to achieve interactive performance
even for the costly final gathering approach.