Abstract:
Computer graphics systems and methods for all-frequency relighting are described. In one described embodiment, all-frequency relighting is achieved by representing low frequencies of lighting with spherical harmonics and approximating the residual high-frequency energy with point lights. In another embodiment low-frequencies are rendered with precomputed radiance transfer (PRT) techniques while the higher-frequencies are rendered with on-the-fly techniques.

Description:
CROSS-REFERENCES 
   This application claims benefit of U.S. Provisional Patent Application No. 60/510,191, filed on Oct. 10, 2003, entitled “ALL FREQUENCY RELIGHTING USING SPHERICAL HARMONICS AND POINT LIGHT DISTRIBUTION,” the contents of which are hereby incorporated herein by reference in their entirety. 
   This application is related by subject matter to the inventions disclosed in the following commonly assigned applications, the entire contents of which are hereby incorporated herein by reference: U.S. patent application Ser. No. 10/815,141, filed on even date herewith, entitled “SYSTEMS AND METHODS FOR ROBUST SAMPLING FOR REAL-TIME RELIGHTING OF OBJECTS IN NATURAL LIGHTING ENVIRONMENTS.” 

   TECHNICAL FIELD 
   The present invention relates generally to the field of computer graphics, and more particularly to real-time re-lighting of computer-generated three-dimensional graphics objects existing in natural lighting environments. 
   BACKGROUND 
   All-frequency lighting generates rich visual effects that cannot be produced with current real-time relighting techniques, which are limited to very hard lights (point lighting) or very smooth lights (spherical harmonics). 
   Many realistic natural environments, such as detailed photographs of building interiors, contain features of all spatial frequencies, and scenes lit by such environments are enriched with subtle details in shadows and highlights. However, these effects are significantly blurred with soft harmonic lighting and become too harsh to be realistic with point lighting. 
   Currently the predominant technique in the art for interactive all-frequency relighting, non-linear approximation in a wavelet lighting basis (NLAWLB), is not practical because of the enormous precomputation costs and storage requirements. Therefore, what is needed is a method for all-frequency relighting that does not suffer the precomputation costs nor require the storage requirements of NLAWLB. 
   SUMMARY 
   Several embodiments of the present invention are directed to systems and methods for all-frequency relighting that use approximations based on radiance accumulation derived from a combination of real-time techniques. In this regard, certain embodiment represent low frequencies of lighting with spherical harmonics and approximate the residual high-frequency energy with point lights. One such embodiment renders low-frequencies with a tabulated rendering method such as the precomputed radiance transfer (PRT) technique (which requires only a moderate amount of precomputation and storage), while the higher-frequencies are rendered with on-the-fly techniques such as shadow maps and shadow volumes. As known and appreciated by those of skill in the art, a tabulated rendering method is one where response to lighting is pre-calculated at a plurality of surface points and represented in some form such as, for example, with a transfer matrix per point. Similarly, and on-the-fly technique is one where the shadowing and lighting effects are generated without pre-calculating or pre-storing any information as to how different points on a surface will respond to each light. 
   Several embodiments of the present invention are also directed to methods that utilize a precomputed radiance transfer (PRT) technique to represent high-frequency residual energy with a distribution of point lights, including but not limited to the PRT techniques disclosed by corresponding application identified in the cross-reference section earlier herein. 
   As is known and appreciated by those of skill in the art that, while rendering with point lights produces hard shadows and highlights, the accumulation of a set of point lights can approximate area light sources quite effectively. 
   In addition, various embodiments of the present invention are directed to a systems and methods for decomposing the lighting into harmonics and sets of point lights. Various alternative embodiments of the present invention are directed to systems and methods for characterizing the types of environments for which the described decomposition is a viable technique in terms of speed (efficiency) versus quality (realism). 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The foregoing summary, as well as the following detailed description of preferred embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the invention, there is shown in the drawings exemplary constructions of the invention; however, the invention is not limited to the specific methods and instrumentalities disclosed. In the drawings: 
       FIG. 1  is a block diagram representing a computer system in which aspects of the present invention may be incorporated; 
       FIG. 2  is a block diagram illustrating a typical computer graphics subsystem; and 
       FIG. 3  is a flow chart illustrating the steps of the method for all-frequency relighting using spherical harmonics and point light distributions. 
   

   DETAILED DESCRIPTION 
   The subject matter is described with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different steps or combinations of steps similar to the ones described in this document, in conjunction with other present or future technologies. Moreover, although the term “step” may be used herein to connote different elements of methods employed, the term should not be interpreted as implying any particular order among or between various steps herein disclosed unless and except when the order of individual steps is explicitly described. 
   Computer Environment 
   Numerous embodiments of the present invention may execute on a computer.  FIG. 1  and the following discussion is intended to provide a brief general description of a suitable computing environment in which the invention may be implemented. Although not required, the invention will be described in the general context of computer executable instructions, such as program modules, being executed by a computer, such as a client workstation or a server. Generally, program modules include routines, programs, objects, components, data structures and the like that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the invention may be practiced with other computer system configurations, including hand held devices, multi processor systems, microprocessor based or programmable consumer electronics, network PCs, minicomputers, mainframe computers and the like. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. 
   As shown in  FIG. 1 , an exemplary general purpose computing system includes a conventional personal computer  20  or the like, including a processing unit  21 , a system memory  22 , and a system bus  23  that couples various system components including the system memory to the processing unit  21 . The system bus  23  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The system memory includes read only memory (ROM)  24  and random access memory (RAM)  25 . A basic input/output system  26  (BIOS), containing the basic routines that help to transfer information between elements within the personal computer  20 , such as during start up, is stored in ROM  24 . The personal computer  20  may further include a hard disk drive  27  for reading from and writing to a hard disk, not shown, a magnetic disk drive  28  for reading from or writing to a removable magnetic disk  29 , and an optical disk drive  30  for reading from or writing to a removable optical disk  31  such as a CD ROM or other optical media. The hard disk drive  27 , magnetic disk drive  28 , and optical disk drive  30  are connected to the system bus  23  by a hard disk drive interface  32 , a magnetic disk drive interface  33 , and an optical drive interface  34 , respectively. The drives and their associated computer readable media provide non volatile storage of computer readable instructions, data structures, program modules and other data for the personal computer  20 . Although the exemplary environment described herein employs a hard disk, a removable magnetic disk  29  and a removable optical disk  31 , it should be appreciated by those skilled in the art that other types of computer readable media which can store data that is accessible by a computer, such as magnetic cassettes, flash memory cards, digital video disks, Bernoulli cartridges, random access memories (RAMs), read only memories (ROMs) and the like may also be used in the exemplary operating environment. 
   A number of program modules may be stored on the hard disk, magnetic disk  29 , optical disk  31 , ROM  24  or RAM  25 , including an operating system  35 , one or more application programs  36 , other program modules  37  and program data  38 . A user may enter commands and information into the personal computer  20  through input devices such as a keyboard  40  and pointing device  42 . Other input devices (not shown) may include a microphone, joystick, game pad, satellite disk, scanner or the like. These and other input devices are often connected to the processing unit  21  through a serial port interface  46  that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, game port or universal serial bus (USB). A monitor  47  or other type of display device is also connected to the system bus  23  via an interface, such as a video adapter  48 . In addition to the monitor  47 , personal computers typically include other peripheral output devices (not shown), such as speakers and printers. The exemplary system of  FIG. 1  also includes a host adapter  55 , Small Computer System Interface (SCSI) bus  56 , and an external storage device  62  connected to the SCSI bus  56 . 
   The personal computer  20  may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer  49 . The remote computer  49  may be another personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the personal computer  20 , although only a memory storage device  50  has been illustrated in  FIG. 1 . The logical connections depicted in  FIG. 1  include a local area network (LAN)  51  and a wide area network (WAN)  52 . Such networking environments are commonplace in offices, enterprise wide computer networks, intranets and the Internet. 
   When used in a LAN networking environment, the personal computer  20  is connected to the LAN  51  through a network interface or adapter  53 . When used in a WAN networking environment, the personal computer  20  typically includes a modem  54  or other means for establishing communications over the wide area network  52 , such as the Internet. The modem  54 , which may be internal or external, is connected to the system bus  23  via the serial port interface  46 . In a networked environment, program modules depicted relative to the personal computer  20 , or portions thereof, may be stored in the remote memory storage device. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
   While it is envisioned that numerous embodiments of the present invention are particularly well-suited for computerized systems, nothing in this document is intended to limit the invention to such embodiments. On the contrary, as used herein the term “computer system” is intended to encompass any and all devices capable of storing and processing information and/or capable of using the stored information to control the behavior or execution of the device itself, regardless of whether such devices are electronic, or mechanical. 
   Graphics Processing Subsystems 
     FIG. 2  is a block diagram illustrating a typical computer graphics subsystem  200 . The subsystem  200  comprises a CPU  21 ′ that, in turn, comprises a core processor  214  having an on-chip L1 cache (not shown) and is further directly connected to an L2 cache  212 . The L1 cache (not shown) of the CPU ′ 21  is usually built onto the microprocessor chip itself, e.g., the Intel MMX microprocessor comes with a 32 KB L1 cache. The L2 cache  212 , on the other hand, is usually on a separate chip (or possibly on an expansion card) but can still be accessed more quickly than RAM, and is usually larger than the L1 cache, e.g., one megabyte is a common size for a L2 cache. As well-known and appreciated by those of skill in the art, the CPU  21 ′ accessing data and instructions in cache memory is much more efficient than having to access data and instructions in random access memory (RAM  25 , referring to  FIG. 1 ), and thus the CPU can achieve significant performance gains that a GPU, which lacks a cache. 
   The CPU  21 ′ is connected to an AGP  230 . The AGP provides a point-to-point connection between the CPU  21 ′, the system memory RAM  25 ′, and graphics card  240 , and further connects these three components to other input/output (I/O) devices  232 —such as a hard disk drive  32 , magnetic disk drive  34 , network  53 , and/or peripheral devices illustrated in FIG.  1 —via a traditional system bus such as a PCI bus  23 ′. The presence of AGP also denotes that the computer system favors a system-to-video flow of data traffic—that is, that more traffic will flow from the CPU  21 ′ and its system memory RAM  25 ′ to the graphics card  240  than vice versa—because AGP is typically designed to up to four times as much data to flow to the graphics card  240  than back from the graphics card  240 . 
   The graphics card  240  further comprises a frame buffer  246  which is directly connected to the display device  47 ′. As well-known and appreciated by those of skill in the art, the frame buffer is typically dual-ported memory that allows a processor (the GPU  242  or the CPU ′ 21 , as the case may be) to write a new (or revised) image to the frame buffer while the display device  47 ′ is simultaneously reading from the frame buffer to refresh (or “update”) the current display content. The graphics card  240  further comprises a GPU  242  and VRAM  244 . 
   The GPU  242  is essentially a second processing unit in the computer system that has been specifically optimized for graphics operations. Depending on the graphics card, the GPU  242  may be either a graphics coprocessor or a graphics accelerator. When the graphics card is a graphics coprocessor, the video driver  224  sends graphics-related tasks directly to the graphics coprocessor for execution, and the graphics coprocessor alone render graphics for the frame buffer  246  (without direct involvement of the CPU  21 ′). On the other hand, when a graphics cards is a graphics accelerator, the video driver  224  sends graphics-related tasks to the CPU  21 ′ and the CPU  21 ′ then directs the graphics accelerator to perform specific graphics-intensive tasks. For example, the CPU  21 ′ might direct the graphics accelerator to draw a polygon with defined vertices, and the graphics accelerator would then execute the tasks of writing the pixels of the polygon into video memory (the VRAMSM  248 ) and, from there, copy the updated graphic to the frame buffer  246  for display on the display device  47 ′. 
   Accompanying the GPU  242  is VRAM  244  that enables the GPU to maintain its own shadow memory (the VRAMSM) close at hand for speedy memory calls (instead of using RAM), and may also provide additional memory (e.g, VWM) necessary for the additional processing operations such as the GPU Method. The VRAM  244  further comprises a VRAMSM  248  and VWM  249 . The VRAMSM  248  is the location in VRAM  244  where the GPU  242  constructs and revises graphic images (including CIs in the GPU Method), and it is the location from which the GPU  242  copies rendered graphic images to the frame buffer  246  of the graphics card  240  to update the display device  47 ′. In the GPU Method, the VWM is an additional area of VRAM that is used by the GPU  242  to temporarily store graphics data that might be used by the GPU  242  to store GOs and/or store/restore POs (or portions thereof) among other things. (By offloading this functionality to the graphics card  240 , the CPU  21 ′ and VSM  222  are freed from these tasks.) 
   The system memory RAM  25 ′ may comprise the operating system  35 ′, a video driver  224 , video memory surfaces (VMSs)  223 , and video shadow memory (VSM)  222 . The VSM is the location in RAM  25 ′ where the CPU  21 ′ constructs and revises graphic images (including CIs in the CPU Method) and from where the CPU  21 ′ copies rendered graphic images to the frame buffer  246  of the graphics card  240  via the AGP  230 . In the CPU Method, the VMSs are additional areas of RAM that are used by the CPU  21 ′ to temporarily store graphics data that might be used by the CPU  21 ′ to store GOs and/or store/restore POs (or portions thereof) among other things. 
   All-Frequency Lighting 
   Traditional ways to represent area lights with points have focused on sets of points that produce still images of acceptable quality, such as by using importance-based metrics that are weighted by the size of the light being approximated. These techniques tend to quantify the size of regions in environment maps with a segmentation technique based on simple lighting intensity stratification. However, since the rendering primitive—that is, rendering with a harsh point light—is inherently localized, very large sets are required in general. Regardless of the importance-based metric employed, several hundred lights are generally needed to represent general environment maps for specific scenes, and generally no less than one hundred points are required to approximate a small area light. For this reason, the broad use of point lights is too expensive for current real-time applications. 
   The methodology employed by several embodiments of the present invention reduce the number of number of point lights required by extracting the low spatial frequencies with spherical harmonics—that is, the frequencies that correspond to the largest areas of energy and which typically carry the highest sampling requirements. This approach, in turn, effectively reduces the number of lights needed since the largest area regions—which usually require the largest numbers of samples for reasonable approximation—will be well approximated by the spherical harmonics. The method segments the environment into regions based on intensity and spatial connectedness, and each of these regions is approximated with some number of point samples per pixel. 
     FIG. 3  is a flow chart illustrating the steps of the method for all-frequency relighting using spherical harmonics and point light distributions, said method described in detail hereinafter. 
   The first step of the method, step  302 , is to segment the environment into strata based on intensity. This may be accomplished by extracting a certain, predefined number of strata with a nearest-intensity, iterative clustering algorithm. This is somewhat different than existing approaches such as, for example, those that partition the dynamic range uniformly into six ranges—the first five corresponding to the first five standard deviations of the signal, the sixth containing the high energy remainder—and groups pixels into each of these six ranges. Though these existing approaches obtain good results in test scenarios, the underlying methodology neglects the fact that many measured environments contain very large dynamic ranges and are probably not well approximated by a normal distribution. In contrast, the present method is instead based on the k-nearest neighbor learning algorithm which is likely to be more robust at stratifying points into well-formed intensity ranges. 
   The second step, step  304 , is to segment the strata into regions by extracting each connected component in each stratum as a separate region. In alternative embodiments, this may also include attempting to group small, neighboring components into single regions. 
   The third step, step  306 , is to prioritizes the regions for subsequent extraction and approximation with small analytic light sources, including without limitation point light sources, small square light sources, and small circular light sources. Of course, there may be several potentially conflicting factors affecting the importance of a region but, in general, regions that extract more high-frequency energy are more important because they contribute more to the outgoing image. Therefore, for this reason, certain embodiments will tend to select smaller regions first because they require fewer samples to be represented well. In any event, several general approaches to determining importance may be employed, including but not limited to: (a) importance based on high-frequency energy reduction (HFER); (b) importance based on iterative algorithm for background estimation (IABE); and (c) the “contrast technique.” Specific examples for each of these techniques is discussed in further detail herein below. 
   The fourth step, step  308 , is to allocate point samples to each region. In this regard, each region is represented with one point sample per pixel before proceeding to the next region, and such regions are chosen until either (a) there are none left to choose or (b) there are no more samples with which to approximate. This approach ensures that each lighting feature is well resolved as it is chosen for inclusion, although it also means that few samples will result in the loss of entire regions. In alternative embodiments, additional noise in all regions may be acceptable if all regions with a minimum number of samples are included. In this regard, additional samples may be added as available to reduce noise in the most critical regions. If temporal jittering successfully turns light reconstruction aliasing into temporal noise, then this solution may be a more principled approach, and thus adding more samples may reduce the noise of the time-integrated output penumbra regions whereas too few samples means unacceptably high temporal noise. 
   Priority/Importance Techniques 
   As mentioned earlier herein, various embodiments of the present invention may employ one or more general techniques for determining importance, including without limitation the following specific technique examples: 
   Importance Based on High-frequency Energy Reduction (HFER): 
   Let p i  for i from 1 to N be the pixels within the region of interest. Let L[p i ] be the value of the original signal at pixel i. Let SH n (L) be the low pass signal obtained by projecting L onto the first n degree spherical harmonics. Furthermore, define the residual high pass signal as follows:
 
 HP   n ( L )= L−SH   n ( L )
 
With these definitions, the HFER importance metric for the region of interest is:
 
   
     
       
         
           
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   This particular metric measures the average reduction in high pass energy over the region. On careful inspection, the method favors smaller regions because of the fact that it measure average reduction, whereas larger regions of the same intensity will have more energy taken out of the high pass signal and hence the average reduction will be lower. 
   Importance Based on Iterative Algorithm for Background Estimation (IABE): 
   The HFER metric assumes that the background value is given by the values of the low pass projection. In certain instances, this has the disadvantage of smearing the energy of a high energy peak into the pixels that surround the peak, and such smearing may be undesirable for at least two reasons: (a) it may cause an overestimation of the background value for dark pixels neighboring a bright peak; and (b) it may overestimate the background underneath the peak itself. 
   An alternative solution is an iterative algorithm (the IABE) that simultaneously extracts peak energy and estimates the background value. At each step of the iteration, the method estimates the value of the background for each pixel to be the value of the low pass filtering of the original signal minus the current estimate of the peaks. The system then updates the estimate of the peaks as the difference between the original signal and the current background estimate. 
   More precisely, the method first assumes that the input lighting signal L will be represented as the sum of a bandlimited signal LSH and a signal based on the sum of point light distributions, L P  as follows:
 
 L≈L   SH   +L   P 
 
In addition, let L SH   k  and L P   k  be the low pass and point distribution signal chosen at iteration k. In particular,
 
             L   P   k     =       ∑     i   =   1     k     ⁢       SQ   ⁡     (     p   i     )       ·     I   i   k               
where SQ(p i ) is an indicator function for inclusion in the pixel of point i, and I i   k  is the chosen intensity of the peak at p i . Then the iterative algorithm of the method is defined by recursive updates of the low pass signal and peak intensities as follows:
 
             L   SH   0     =       SH   n     ⁡     (   L   )                     I   i   0     =       (     L   -       SH   n     ⁡     (   L   )         )     ⁡     [     p   i     ]                     L   SH   k     =       SH   n     ⁡     (     L   -     L   p     k   -   1         )                     I   i   k     =       (     L   -     L   SH   k       )     ⁡     [     p   i     ]             
Thus the modified importance metric for the region of interest is as follows:
 
             1   N     ⁢       ∑     i   =   1     N     ⁢       (       L   P     ⁡     [     p   i     ]       )     2             
Importance Based on Contrast:
 
   Another potential drawback of the IABE importance metric defined above is that it does not account for the perceptual effect that a dark, localized region in a bright field, although this is not as important to resolve as a bright localized region in a dark field. Whereas the former generates an image with flat lighting and small amount of illumination subtracted, the latter generates a stark image where the bright region may cast strong shadows. In any event, one way to alleviate this problem (and to discriminate between the two cases described above in the correct way) would be to modify the metric to take into account the contrast of the pixels in the region relative to the background rather than just high frequency reduction. Thus a further refinement of the importance for the region of interest, based on the IABE technique, is as follows (referred to herein simply as the “contrast technique”): 
   
     
       
         
           
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   CONCLUSION  
   The various system, methods, and techniques described herein may be implemented with hardware or software or, where appropriate, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention. In the case of program code execution on programmable computers, the computer will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. One or more programs are preferably implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language, and combined with hardware implementations. 
   The methods and apparatus of the present invention may also be embodied in the form of program code that is transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via any other form of transmission, wherein, when the program code is received and loaded into and executed by a machine, such as an EPROM, a gate array, a programmable logic device (PLD), a client computer, a video recorder or the like, the machine becomes an apparatus for practicing the invention. When implemented on a general-purpose processor, the program code combines with the processor to provide a unique apparatus that operates to perform the indexing functionality of the present invention. 
   While the present invention has been described in connection with the preferred embodiments of the various figures, it is to be understood that other similar embodiments may be used or modifications and additions may be made to the described embodiment for performing the same function of the present invention without deviating there from. For example, while exemplary embodiments of the invention are described in the context of digital devices emulating the functionality of personal computers, one skilled in the art will recognize that the present invention is not limited to such digital devices, as described in the present application may apply to any number of existing or emerging computing devices or environments, such as a gaming console, handheld computer, portable computer, etc. whether wired or wireless, and may be applied to any number of such computing devices connected via a communications network, and interacting across the network. Furthermore, it should be emphasized that a variety of computer platforms, including handheld device operating systems and other application specific hardware/software interface systems, are herein contemplated, especially as the number of wireless networked devices continues to proliferate. Therefore, the present invention should not be limited to any single embodiment, but rather construed in breadth and scope in accordance with the appended claims.