Microsoft Technology Licensing, LLC

James Frederick Blinn

Charles T Loop

John Carlton Platt

J Turner Whitted

Donald P Mitchell

Michael David Marr

Michael Dean Stokes

John Michael Snyder

Nicholas P Wilt

Gideon Andreas Yuval

Jerome E Lengyel

Adrian Secchia

Steven A Gabriel

Andrew C Godfrey

Alvy Ray Smith

Surfaces defined by B&#xe9;zier tetrahedron are generated on programmable graphics hardware. Custom programmed vertex processing, performed by either the CPU or the GPU includes the computation of a symmetric tensor and the assignment of the unique elements of the computed symmetric tensor as vertex attribute data. The vertex attribute data is interpolated by the graphics hardware and output to custom programmed pixel processing. The pixel processing uses the interpolated vertex attribute data to reconstruct, at each pixel, the symmetric tensor which enables the determination of the roots of the polynomial defining the surface to be generated. If no real roots exist, the pixel processing can exit early. If the roots of the polynomial exist, the smallest root can be used as the basis for computing a normal to a point on the surface being rendered, enabling the determination of the color and depth of that pixel.

Real-time GPU rendering of piecewise algebraic surfaces

A shape defined partially be a B&#xe9;zier curve is rendered through a GPU-implemented technique which determines for various screen points which side of the curve the points lie on. This is done in the particular case of cubic B&#xe9;zier curves for shapes defined by the B&#xe9;zier control points of the curves. The type of the curve is identified through an analysis of an inflection point polynomial based on the curve. The curve is then projected to a canonical implicit form in a canonical texture space, allowing computation to be efficiently performed on a simple canonical form of the curve.

Resolution-independent curve rendering using programmable graphics hardware

Surfaces defined by a B&#xe9;zier tetrahedron, and in particular quadric surfaces, are rendered on programmable graphics hardware. Pixels are rendered through triangular sides of the tetrahedra and locations on the shapes, as well as surface normals for lighting evaluations, are computed using pixel shader computations. Additionally, vertex shaders are used to aid interpolation over a small number of values as input to the pixel shaders. Through this, rendering of the surfaces is performed independently of viewing resolution, allowing for advanced level-of-detail management. By individually rendering tetrahedrally-defined surfaces which together form complex shapes, the complex shapes can be rendered in their entirety.

Resolution-independent surface rendering using programmable graphics hardware

A method for providing a 16-bit floating point data representation where the 16-bit floating point data representation may be operated upon by a microprocessors native floating point instruction set. The method contemplates the use a variety of techniques for converting the 16-bit floating point number into a representative native floating point value. Thereafter, the native microprocessor floating point instruction set may perform operations upon the converted data. Upon completion, the native floating point data representation may be converted back into the 16-bit floating point value.

System and method for using native floating point microprocessor instructions to manipulate 16-bit floating point data representations

Image data processing and image rendering methods and systems whereby images are displayed on display devices having pixels with separately controllable pixel sub-components. Image data, such as data encoded in a three-channel signal, is passed through a low-pass filter to remove frequencies higher than a selected cutoff frequency, which obtain samples from the color components of the signal that map spatially different image regions to individual pixel sub-components. It has been found that color aliasing effects can be significantly reduces at a cutoff frequency somewhat higher than the Nyquist frequency, while enhancing the spatial resolution of the image. The image data is then passed through sampling filters, A generalized set of filters includes nine filters, one for each combination of one color and one pixel sub-component. The filtering coefficients of the filters can be selected to optimize of approximate an optimization of an error metric, which represents the color and luminance errors perceived on the display device. In this manner, a desired balance between color accuracy and luminance accuracy can be obtained. The samples mapped to individual pixel sub-components are used to generate luminous intensity values for the displayed image.

Filtering image data to obtain samples mapped to pixel sub-components of a display device

Image data processing and image rendering methods and systems whereby images are displayed on display devices having pixels with separately controllable pixel sub-components. Image data, such as data encoded in a three-channel signal, is passed through a low-pass filter to remove frequencies higher than a selected cutoff frequency, which obtain samples from the color components of the signal that map spatially different image regions to individual pixel sub-components. It has been found that color aliasing effects can be significantly reduces at a cutoff frequency somewhat higher than the Nyquist frequency, while enhancing the spatial resolution of the image. The image data is then pass through sampling filters, A generalized set of filters includes nine filters, one for each combination of one color and one pixel sub-component. The filtering coefficients of the filters can be selected to optimize of approximate an optimization of an error metric, which represents the color and luminance errors perceived on the display device. In this manner, a desired balance between color accuracy and luminance accuracy can be obtained. The samples mapped to individual pixel sub-components are used to generate luminous intensity values for the displayed image.

An optimal filter kernel, formed by convolving a box filter with a filter of fixed integer width and unity area, is used to perform image resizing and reconstruction. The optimal filter has forced zeros at locations along a frequency scale corresponding to the reciprocal of the spacing of one or more pixels that comprise a source image to be resized. When a rescale value for a source image is selected, the optimal filter kernel is computed, mapped to the source image, and centered upon a location within the source image corresponding to the position of an output pixel to be generated. The number of pixels that lie underneath the optimal filter kernel is established by multiplying the number of pixels that comprise the width of the source image by the selected rescale value. Upon mapping the optimal filter kernel, the output pixel values that comprise the resized image are then evaluated by processing the one or more source image pixels, such as through interpolation. Alternatively, the output pixel values of the resized image are calculated by performing partial integral analysis with respect to a standard filter kernel of fixed width and unity area. The output pixel values are calculated by multiplying the pixel value for each pixel under the kernel by the area of the standard filter kernel surrounding the pixel. The products are then summed to reveal the output pixel value, and placed into the output image buffer. Both of these methods speed up the computation process, while producing a ripple free output image.

Generating resized images using ripple free image filtering

A method for pulling (extracting) a matte of an image of a foreground object from a composite image, using a computer. An image of the foreground object is recorded over at least two backgrounds having arbitrarily different coloring. Each point of one background must have a color that is different than the color of a corresponding point in the other backgrounds. The images may be recorded with an analog camera and digitized with a scanner or recorded with a digital camera. Images should be registered during recording and digitization to eliminate misalignment of corresponding points in each recorded image. A triangulation of corresponding points of each recorded image is performed so that an alpha value (opacity) and a set of color coordinates for each point of an uncomposited image of the foreground object may be determined using either a difference of sums technique or a least squares technique. To facilitate the triangulation, images are recorded of each background alone, without the foreground object. The lighting and distances between the camera and object, and between the object and backgrounds should remain constant during the image recording process.

Extracting a matte of a foreground object from multiple backgrounds by triangulation

An image generator takes graphical objects and an occlusion relationship for the objects and resolves non-binary occlusion cycles with image compositing operations to produce an output image of the objects. The image generator takes an occlusion relationship for objects in a scene and a set of antialiased image layers with transparency of the objects and produces an antialiased image of the objects with hidden surfaces eliminated. One implementation operates on subsets of the objects in a scene that form non-binary cycles. This implementation uses a chain of atop operators to combine occluding objects with a selected object from a subset, and then combines this result with other objects in the cycle using over image operations. Another implementation computes a chain of out image operations for each object to combine the image layers of the occluding objects with the image layer of the object. The results of each chain of out image operations are summed to produce an output image.

Image generator and method for resolving non-binary cyclic occlusions with image compositing operations

A method for simulating fog in 3D graphics rendering applications correctly computes fogged pixel colors even in graphics scenes where two surfaces overlap and the frontmost surface is partially transparent. The method computes the fog for each surface according to the following formula: &fnof;F atop A, where &fnof; is the amount of fog, F is the color of the fog, and A is the color of the pixel being fogged. Each fogged surface can be rendered independently to a separate image layer, called a fogged image layer. The graphics rendering system can then simulate the motion of a fogged image layer by moving the fogged layer in an (x,y) plane without re-computing the fogged pixels, or by moving the fogged layer in the z-direction and independently re-computing the moving fogged layer with a new value for the amount of fog applied to the image layer. The method applies to scenes where there are several fogged layers and to scenes that simulate fire and glow with pixels that are totally transparent but have non-zero color values.

Fog simulation for partially transparent objects

A scaling multiplier circuit in accordance with the invention includes a multiplier circuit, a carry calculation circuit, a logic circuit, and an adder circuit. The multiplier circuit produces a 16-bit product of two 8-bit input numbers. The 16-bit product has bits m(15:0). The carry calculation circuit produces a first carryout bit from a sum of a first number consisting of bits m(6:0), a second number consisting of bits m(14:8), and a third number consisting of bit m(7). The logic circuit produces intermediate carryout bits from a sum of bit m(7m), m(15), the first carryout bit, and a constant bit having a value of '1'. The adder circuit produces the actual scaled product by summing the intermediate carryout bits and a fourth number consisting of bits m(15:8).

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Bellevue, WA, United States of America

James Frederick Blinn