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Soft Shadows using Hardware Cameras

This paper explores innovative methods for generating soft shadows in computer graphics by leveraging hardware cameras. Building on previous approaches such as cone tracing and geometry-based volume algorithms, it presents a new technique that utilizes graphics hardware to simulate the projection of multiple rays more efficiently. By examining various grid configurations, the methodology demonstrates a significant performance improvement while maintaining superior shadow quality. The work highlights the challenges faced in feedback loops and the potential for enhanced parallelization using CPU and GPU resources.

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Soft Shadows using Hardware Cameras

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  1. Soft Shadows using Hardware Cameras Kyle Moore COMP 870

  2. Previous Work • Cone Tracing • Amanatides, J. 1984. Ray tracing with cones. In Proceedings of the 11th Annual Conference on Computer Graphics and interactive Techniques H. Christiansen, Ed. SIGGRAPH '84. ACM Press, New York, NY, 129-135. • Uses cones shaped rays instead of lines to calculate light intersections with objects • Lots of difficult computations

  3. Previous Work • Forward Hardware-Based Techniques • Assarsson, U. and Akenine-Möller, T. 2003. A geometry-based soft shadow volume algorithm using graphics hardware.ACM Trans. Graph. 22, 3 (Jul. 2003), 511-520. • Uses silhouette edges to case penumbra volumes which are to create shadow maps • This is an approximation and can be inaccurate

  4. Previous Work • Backwards silhouette edge detection • Laine, S., Aila, T., Assarsson, U., Lehtinen, J., and Akenine-Möller, T. 2005. Soft shadow volumes for ray tracing. ACM Trans. Graph. 24, 3 (Jul. 2005), 1156-1165. • Uses a hemicube acceleration structure to find silhouette edges to calculate the amount of light occlusion • Doesn’t take advantage of hardware

  5. My idea • My Inspiration • While working on my ray tracer • Unhappy with soft shadows • Wanted to shoot more rays, but high cost • New approach • Let graphics hardware simulate shooting many rays • Perhaps get performance increase as well as quality enhancement

  6. Camera’s View Average Pixels to get Occlusion percentage My Approach Area Light Source Occluder Surface View Frustum Hardware Camera

  7. My Approach Camera’s View Area Light Source Occluder Surface

  8. My Application

  9. Results - Quality Old approach 4x4 grid My Approach 4x4 grid

  10. Results - Quality Old approach 17x17 grid My Approach 17x17 grid

  11. Results - Performance My Approach 32x32 grid 96 seconds Old approach 17x17 grid 152 seconds

  12. Performance

  13. Room for improvement • Each pixel draws a frame and the fetches the results • Feedback loops are slow in hardware • Does not take advantage of parallelization of using CPU and GPU at the same time

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