Method and system for scattered spherical harmonic approximation
View Patent ↗A scattered spherical harmonic (SH) approximation method is proposed for pre-computation of SH coefficients at sampling vertices of an object to be rendered. The approach sparsely populates the object with a plurality of SH sampling vertices and uses the least squares approximation to calculate the SH coefficients at any (other) arbitrary point of the object by extrapolating from the computed SH coefficients at the SH sampling vertices.
1. A system to support scattered spherical harmonic (SH) approximation, comprising:
a sampling module operable to select an initial subset of sampling points related to an object to be rendered from a substantially uniform distribution of sampling points,
the initial subset of sampling points selected based at least partly on density variations of a volume,
the initial subset of sampling points varying in density,
further wherein a first subset of the initial subset of sampling points is selected at least partly to be inside a surface of the object and a second subset of the initial subset of sampling points is selected at least partly to be over the surface of the object such that a combination of the first subset and the second subset substantially suppresses oscillations from a fitting polynomial associated with the sampling module;
a pre-computing module operable to:
compute, at each of the initial subset of sampling points, a set of SH coefficients for each of one or more functions used for rendering the object, wherein SH coefficients are not computed at the non-selected sampling points; and
approximate, based on the set of SH coefficients at the initial subset of sampling points, a set of SH coefficients for each of the one or more functions at any other point; and
a real-time rendering module operable to render global illumination effect of the object at run time based on the one or more functions.
2. The system of claim 1 , wherein:
the sampling module is operable to select few of the initial subset of sampling points where the density variations are smooth.
3. The system of claim 1 , wherein:
the sampling module is operable to select many of the initial subset of sampling points where the density variations are great.
4. The system of claim 1 , wherein:
the sampling module is operable to select none of the initial subset of sampling points in a region far away from the object.
5. The system of claim 1 , wherein:
the one or more functions include a transfer function representing local visibility of the object and an incoming light function representing a lighting source.
6. The system of claim 1 , wherein:
The lighting source is a high frequency lighting source.
7. The system of claim 1 , wherein:
the pre-computing module is operable to compute and/or approximate the set of SH coefficients for one or more functions before rendering the object.
8. The system of claim 1 , wherein:
the pre-computing module is operable to compute the set of SH coefficients for the one or more functions at each of the initial subset of sampling points via Monte Carlo integration technique.
9. The system of claim 1 , wherein:
the pre-computing module is operable to approximate the set of SH coefficients for the one or more functions at any other point via least squares analysis.
10. The system of claim 9 , wherein:
the pre-computing module is operable to approximate the set of SH coefficients for the one or more functions at any other point via nth-order polynomial approximation.
11. The system of claim 1 , wherein:
a real-time rendering module is further operable to render the object at run time by computing the dot product of the SH coefficients of the one or more functions.
12. The system of claim 1 , wherein:
the global illumination effect takes into account subtle effects through different types of diffuse lighting models, which include any of un-shadowed, shadowed, and inter-reflected.
13. A method to support scattered spherical harmonic (SH) approximation, comprising:
selecting, using a sampling module, an initial subset of sampling points related to an object to be rendered from a substantially uniform distribution of sampling points,
the initial subset of sampling points selected based at least partly on density variations of a volume;
the initial subset of sampling points varying in density,
the density variations of the initial subset of sampling points corresponding to the density variations of the volume,
further wherein a first subset of the initial subset of sampling points is selected at least partly to be inside a surface of the object and a second subset of the initial subset of sampling points is selected at least partly to be over the surface of the object such that a combination of the first subset and the second subset substantially suppresses oscillations from a fitting polynomial associated with the sampling module;
using a pre-computing module to:
compute, at each of the initial subset of sampling points, a set of SH coefficients for each of one or more functions used for rendering the object, wherein SH coefficients are not computed at the non-selected sampling points; and
approximate, based on the set of SH coefficients at the initial subset of sampling points, a set of SH coefficients for each of the one or more functions at any other point;
rendering global illumination effect of the object at run time based on the one or more functions.
14. The method of claim 13 , further comprising: selecting few of the initial subset of sampling points where the density variations are smooth.
15. The method of claim 13 , further comprising: selecting many of the initial subset of sampling points where the density variations are great.
16. The method of claim 13 , further comprising: selecting none of the initial subset of sampling points in a region far away from the object.
17. The method of claim 13 , further comprising:
computing and/or approximating the set of SH coefficients for one or more functions before rendering the object.
18. The method of claim 13 , further comprising:
computing the set of SH coefficients for the one or more functions at each of the initial subset of sampling points of the object via Monte Carlo integration technique.
19. The method of claim 13 , further comprising:
approximating the set of SH coefficients for the one or more functions at any other point via least squares analysis.
20. The method of claim 19 , further comprising:
approximating the set of SH coefficients for the one or more functions at any other point via nth-order polynomial approximation.
21. A non-transitory computer-readable data storage medium having instructions stored thereon that when executed cause a system to:
select, using a sampling module, an initial subset of sampling points related to an object to be rendered from a substantially uniform distribution of sampling points,
the initial subset of sampling points selected based at least partly on density variations of a volume,
the initial subset of sampling points varying in density,
further wherein a first subset of the initial subset of sampling points is selected at least partly to be inside a surface of the object and a second subset of the initial subset of sampling points is selected at least partly to be over the surface of the object such that a combination of the first subset and the second subset substantially suppresses oscillations from a fitting polynomial associated with the sampling module;
use a pre-computing module to:
compute, at each of the initial subset of sampling points, a set of SH coefficients for each of one or more functions used for rendering the object, wherein SH coefficients are not computed at the non-selected sampling points; and
approximate, based on the set of SH coefficients at the initial subset of sampling points, a set of SH coefficients for each of the one or more functions at any other point; and
render global illumination effect of the object at run time based on the one or more functions.
22. A system to support scattered spherical harmonic (SH) approximation, comprising:
a sampling module operable to select a subset of sampling points from a substantially uniform distribution of sampling points related to an object to be rendered, the selected subset of sampling points not being uniform, wherein the sampling points in a lower density variation first volume are selected with lower frequency and the sampling points in a higher density variation second volume are selected with higher frequency;
a pre-computing module operable to:
compute, at each of the subset of sampling points, a set of SH coefficients for each of one or more functions used for rendering the object, wherein SH coefficients are not computed at the non-selected sampling points; and
approximate, at any other point and based on the set of SH coefficients at the subset of sampling points, a set of SH coefficients for each of the one or more functions; and
a real-time rendering module operable to render global illumination effect of the object at run time based on the one or more functions.
23. A system to support scattered spherical harmonic (SH) approximation, comprising:
a sampling module operable to select a subset of grid points from a substantially uniform distribution of grid points in a scene to be rendered, wherein the grid points in a lower density variation first region of the scene are selected with lower frequency and the grid points in a higher density variation second region of the scene are selected with higher frequency;
a pre-compute module operable to:
compute a set of SH coefficients at the selected grid points for a function used for rendering the object, wherein SH coefficients are not computed at the non-selected grid points; and
approximate a set of SH coefficients for the function at any other point and based on the pre-computed SH coefficients at the selected grid points; and
a real-time rendering module operable to render global illumination effect of the object at run time based on the function.
24. The system of claim 23 , wherein the selected grid points form a sparse, scattered 3D data set, wherein the pre-compute module uses least squares analysis to approximate the set of SH coefficients for the function at the any other point.