Light Map Compression

• US 20070200848A1
• Filed: 01/24/2006
• Published: 08/30/2007
• Est. Priority Date: 01/24/2006
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Hu, Yaohua
• Granted Patent: US 7,593,011 B2
• Time in Patent Office: Days
• US Class Current: 345/426
• CPC Class Codes: G06T 15/50 Lighting effects

Abstract

A technique to reduce the size of a light map using variable sampling is provided. A surface is identified and divided into various regions in accordance with the frequencies associated with the regions. Different sampling rates for lighting information are allocated to the regions based on the different frequencies associated with the regions. The lighting information is re-sampled in accordance with the allocated sampling rates into a light map.

Claims

1. One or more device-readable media encoded with device-executable instructions for performing steps comprising:

identifying a surface associated with a graphical rendering environment;

calculating frequencies associated with areas of the surface, each frequency representing complexity of features in the associated area;

dividing the surface into regions based, at least in part, on the calculated frequencies;

allocating a sampling rate to each region based, at least in part, on the frequency corresponding to the region;

sampling lighting data in each region with the allocated sampling rate; and

creating a light map associated with the surface based, at least in part, on the sampled lighting data.

2. The one or more device-readable media as recited in claim 1, further comprising determining edges of the regions based on at least one of texture space Laplacian or gradient relative variance per triangle in the surface.

3. The one or more device-readable media as recited in claim 1, further comprising separating the surface into charts along edges associated with each region.

4. The one or more device-readable media as recited in claim 3, further comprising maximizing a compression ratio associated with each chart.

5. The one or more device-readable media as recited in claim 3, further comprising balancing an area associated with each triangle within each chart.

6. The one or more device-readable media as recited in claim 5, further comprising modifying texture coordinates of vertices (vertex uv) in each chart to balance the chart.

7. The one or more device-readable media as recited in claim 6, wherein the vertex uv are modified based on at least one of texture space Laplacian or gradient relative variance.

8. The one or more device-readable media as recited in claim 3, further comprising balancing sampling rates associated with the charts of the surface based, at least in part, on the frequency associated with each chart.

9. The one or more device-readable media as recited in claim 8, further comprising balancing the sampling rates based, at least in part, on the local integrated metric tensor (IMT) associated with each chart of the surface.

10. One or more device-readable media encoded with device-executable components comprising:

a graphical objects manager configured to manage objects associated with the graphics generated by an application, the graphical objects manager also configured to generate surface data that represents a surface of at least one of the objects;

a lighting manager configured to determine lighting data provided by the application; and

a light map generator configured to receive lighting and surface data from the lighting manager and the graphical object manager, the light map generator also configured to determine frequencies associated with areas of the surface to tessellate the surface into regions based on the determined frequencies, the light map generator further configured to allocate a sampling rate for each region based on the frequency associated with the region and to sampling the regions with the allocated sampling rates to generate a light map corresponding to the surface.

11. The one or more device-readable media as recited in claim 10, wherein the surface data includes a mesh of polygons that represent surfaces of the graphical objects.

12. The one or more device-readable media as recited in claim 10, wherein the lighting data includes at least one of sources or directions of lighting associated with the graphical objects.

13. The one or more device-readable media as recited in claim 10, wherein the frequencies determined by the light map generator represent complexity of features in each region.

14. The one or more device-readable media as recited in claim 13, wherein the frequencies are determined by at least one of texture space Laplacian, gradient relative variance per triangle of the surface, or frequency analytical method.

15. The one or more device-readable media as recited in claim 10, wherein the light map generator is configured to balance the sampling rates the regions so that the balanced sampling rates do not cause a total size of the light map to exceed an allocated maximum size.

16. An apparatus comprising:

means for identifying lighting and surface data associated with a surface in a rendering environment;

means for determining frequencies associated with areas of the surface;

means for tessellating the surface into regions based on the determined frequencies;

means for allocating a sampling rate to each region based on the frequency corresponding to the region;

means for sampling lighting data in each region with the allocated frequency; and

means for creating a light map for the surface based on the sampled lighting data.

17. The apparatus as recited in claim 16, further comprising means for tessellating the surface based on at least one of texture space Laplacian or gradient relative variance per triangle in the surface.

18. The apparatus as recited in claim 16, further comprising means for performing geometry optimization.

19. The apparatus as recited in claim 16, further comprising means for performing sub-chart optimization.

20. The apparatus as recited in claim 19, further comprising:

means for performing intra-chart optimization;

means for performing inter-chart optimization;

means for performing cluster optimization; and

means for re-sampling the light map.