Virtual crowd rendering have been used in film industry with offine rendering methods for a long time. But its existence in interactive real-time applications such as video games is not so common due to the limited rendering power of current graphics hardware. This thesis describes a novel method to improve shadow mapping performance of a crowded scene by taking into account the screen space visibility of the casted shadow of a crowd instance when rendering the shadow maps. A grid-based visibility mask creation method is proposed which is irrelevant to scene complexity. This improves the rendering performance especially when there are many occluded instances of the crowd which is a common scenario in urban environments and accelerates the usage of crowds in real time applications, such as games. We compute visibility of all agents in a crowd in parallel on the graphics processing unit(GPU) without having a requirement of a stencil buer or light direction dependent shadow mask. Technique also improves the view space rendering time by reducing the visibility check cost of the agents that are located on the invisible areas of the scene.
The methodology introduced in this thesis gets more effective in each shadow map rendering pass by re-using the same visibility mask for shadow caster culling and enables many local lights with shadows. We also give a brief information about the state of the art of crowd rendering and shadowing, explaining how suitable the method with the implementations of different shadow mapping approaches. The technique is very well compatible with the modern crowd rendering techniques such as skinned instancing, dynamic level of detail(LOD) determination and GPU-based simulation.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12615107/index.pdf |
| Date | 01 November 2012 |
| Creators | Kocdemir, Sahin Serdar |
| Contributors | Isler, Veysi Assoc.prof.dr. |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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