Rendering - Image-Based Rendering

Plenoptic Modeling

Image-based methods [LEVO96] [GORT96] offer a wholly new approach using essentially high-dimensional interpolation, which sidesteps most of the problems that make inversion ill-posed. The recent success of these methods suggests that they are a viable alternative to inversion when geometry capture is not the explicit objective, that is, when the goal is simply to generate new images [CHEN95] [MCMI95] [TORB96]. We are exploring the theoretical limits of image-based rendering using a number of tools from approximation theory, in a manner analogous to our previous efforts to analyze global illumination.

Data obtained from Cornell's light measurement laboratory is likely to be instrumental in closing the gap between theory and practice. For example, we will need to characterize several aspects of actual BRDFs, such as the maximum reflectivity and the spread of the specular peak, to verify error bounds for a range of interpolated images constructed from a sequence of images. This is an excellent opportunity to combine measurement and analysis and should provide a better foundation for future algorithms.

Image-based rendering is an important new approach to rapid rendering of highly realistic images. A typical image-based approach is to use cameras to collect real-world information, and then to use discrete reference images to create new views within a continuous range by interpolation, typically image warping. The advantage of image-based rendering over polygon rendering is the ability to display natural, real-world (as well as synthetic) scenes at rendering speeds that are independent of scene complexity.

The Center became an early leader in image-based rendering with the plenoptic modeling work of Leonard McMillan and Gary Bishop at UNC, and continues to pursue the field vigorously [MCMI95C].

Plenoptic modeling addresses the problem of precise range information but sparse set camera images and can move back and forth along this spectrum, depending on implementation needs.