1. Real-time Graphics for VR Chapter 23

2. What is it about? In this part of the course we will look at how to render images given the constrains of VR: –we want realistic models, eg scanned humans, radiosity solution of the environment etc (lots of polygons/textures) –we need real-time rendering over 25 frames per second often maintaining the frame rate is more important than image quality

3. How can we accelerate the rendering? Using graphics hardware that can do the intensive operations in special chips –as processing power increases so do user expectations Fine tuning the models –removing overlapping parts of polygons –removing un-needed polygons (undersides etc) –replacing detail with textures Improving the graphics pipeline – This is what we will concentrate

4. Making the most of the graphics hardware Know the strengths and limitation of your hardware –multipass texturing –display lists, etc Don’t compromise the portability, if software to be used on other platforms Be aware of the rapid changes in technology –eg bandwidth vs rendering speed

5. What’s wrong with the standard graphics pipeline It processes every polygon therefore it does not scale According to the statistics, the size of the average 3D model grows more than the processing power

6. We can use several acceleration techniques which can be broadly put into 3 categories: Visibility culling –avoid processing anything that will not be visible in (and thus not contribute to) the final image Levels of detail –generate several representations for complex objects are use the simplest that will give adequate visual result from a given viewpoint Image based rendering –replace complex geometry with a texture

7. Constant frame rate The techniques above are not enough to assure it We need a system load management – it will try to achieve an image with the best quality possible given within the give frame time –if there is too much load on the system it will resolve to drastic actions (eg drop objects) –it’s an NP complete problem

8. The Visibility Problem Select the (exact?) set of polygons from the model which are visible from a given viewpoint Average number of polygons, visible from a viewpoint, is much smaller than the model size

9. Visibility Culling Avoid rendering polygons or objects not contributing to the final image We have three different cases of non-visible objects: –those outside the view volume (view volume culling) –those which are facing away from the user (back face culling) –those occluded behind other visible objects (occlusion culling)

10. Visibility Culling

11. Visibility methods Exact methods –Compute all the polygons which are at least partially visible but only those Approximate methods –Compute most of the visible polygons and possibly some of the hidden ones Conservative methods –Compute all visible polygons plus maybe some hidden ones

12. View volume culling Assuming the scene is stored into some sort of spatial subdivision We already saw many earlier in the course, some examples: –hierarchical bounding volumes / spheres –octrees / k-d trees / BSP trees –regular grid

13. View volume culling Compare the scene hierarchically against the view volume When a region is found to be outside the view volume then all objects inside it can be safely discarded If a region is fully inside then render without clipping What is the difference with clipping?

14. View volume culling against a bounding volume hierarchy

15. View volume culling against a space partitioning hierarchy

16. View volume culling Easy to implement A very fast computation Very effective result Therefore it is included in almost all current rendering systems

17. Back-face culling Simplest version is to do it per polygon –just test the normal of each polygon against the direction of view (eg dot product) More efficient methods operate on clusters of polygons –group polygons using the direction of their normals, make a table –compare the view direction against the entries in this table

18. Occlusion culling By far the most complex (and interesting) of the three, both in terms of algorithmic complexity and in terms of implementation This is because it depends on the inter-relation of the objects Many different algorithms have been proposed, each one is better for different types of models What’s the difference with HRS