We compress storage and accelerate performance of precomputed radiance transfer (PRT), which captures the way an object shadows, scatters, and reflects light. PRT records over many surface points a transfer matrix. At run-time, this matrix transforms a vector of spherical harmonic coefficients representing distant, low-frequency source lighting into exiting radiance. Per-point transfer matrices form a high-dimensional surface signal that we compress using clustered principal component analysis (CPCA), which partitions many samples into fewer clusters each approximating the signal as an affine subspace. CPCA thus reduces the high-dimensional transfer signal to a low-dimensional set of perpoint weights on a per-cluster set of representative matrices. Rather than computing a weighted sum of representatives and applying this result to the lighting, we apply the representatives to the lighting per-cluster (on the CPU) and weight these results perpoint (on the GPU). Since the output of the matrix is lowerdimensional than the matrix itself, this reduces computation. We also increase the accuracy of encoded radiance functions with a new least-squares optimal projection of spherical harmonics onto the hemisphere. We describe an implementation on graphics hardware that performs real-time rendering of glossy objects with dynamic self-shadowing and interreflection without fixing the view or light as in previous work. Our approach also allows significantly increased lighting frequency when rendering diffuse objects and includes subsurface scattering.
Summary Core photographs become more valuable when scanned and digitized into a data base. The ability to zoom, enhance the color contrasts and compare directly to other log and well data allows details to be identified and interpreted that are barely visible on the original photographs. Examples of core images are shown that compare various photograph scales and various scanning densities to illustrate effects on image quality. Core images are compared to micro-resistivity and acoustic images demonstrating the interpretations available by combining various data sets. Thin section and SEM images, which have been stored in the same data base, are also displayed. The various steps in making digital images of core photographs are discussed including core preparation, photography and transfer of data sets. High quality digital images produce large blocks of data. Data set sizes are compared for variations in scanning density, photographic scale and color resolution. Some applications are discussed that use quantified image data including thin bed corrections, definition of pore geometry and improved relative depth control. Introduction Image data represents the only remaining well data to be routinely stored in digital data bases. There are two main reasons for this delay. The quality of good photographs (the most common media for storing images) is difficult to capture and when using high density scanners in an attempt to capture this quality, the sizes of the resulting data sets become very large. However the benefits of digital images accrue very quickly, even with existing technology. These benefits included those attributable to all digital data bases, such quick and easy distribution and access. In addition images derived from photographs can be manipulated to yield information not available from the original images. This is realized by color enhancement, zooming and by combining images to make a synthetic image. It also opens the option of quantifying image characteristics to reflect reservoir properties.
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