Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
Interactive rendering with arbitrary BRDFs using separable approximations
ACM SIGGRAPH 99 Conference abstracts and applications
Light field mapping: efficient representation and hardware rendering of surface light fields
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Homomorphic factorization of BRDF-based lighting computation
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Evaluation of tone mapping operators using a High Dynamic Range display
ACM SIGGRAPH 2005 Papers
Compressing and companding high dynamic range images with subband architectures
ACM SIGGRAPH 2005 Papers
High-Dynamic-Range Still-Image Encoding in JPEG 2000
IEEE Computer Graphics and Applications
High dynamic range texture compression for graphics hardware
ACM SIGGRAPH 2006 Papers
High dynamic range texture compression
ACM SIGGRAPH 2006 Papers
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Surface structures at meso- and micro-scale are almost impossible to convincingly reproduce with analytical BRDFs. Therefore, image-based methods like light fields, surface light fields, reflectance fields and bidirectional texture functions became widely accepted to represent spatially nonuniform surfaces. For all of these techniques a set of input photographs from varying view and/or light directions is taken that usually by far exceeds the available graphics memory. The recent development of HDR photography additionally increased the amount of data generated by current acquisition systems since every image needs to be stored as an array of floating point numbers. Furthermore, statistical compression methods -- like principal component analysis (PCA) -- that are commonly used for compression are optimal for linearly distributed values and thus cannot handle the high dynamic range radiance values appropriately. In this paper, we address both of these problems introduced by the acquisition of high dynamic range light and reflectance fields. Instead of directly compressing the radiance data with a truncated PCA, a non-linear transformation is applied to input values in advance to assure an almost uniform distribution. This does not only significantly improve the approximation quality after an arbitrary tone mapping operator is applied to the reconstructed HDR images, but also allows to efficiently quantize the principal components and even apply hardware-supported texture compression without much further loss of quality. Thus, in addition to the improved visual quality, the storage requirements are reduced by more than an order of magnitude.