Fast Depth-of-Field Rendering with Surface Splatting

1. Fast Depth-of-Field Rendering with Surface Splatting Jaroslav Křivánek CTU PragueIRISA – INRIA Rennes Jiří Žára CTU Prague Kadi Bouatouch IRISA – INRIA Rennes Computer Graphics Group

2. Goal • Depth-of-field rendering with point-based objects • Why point-based ? • Efficient for complex objects • Why depth-of-field ? • Nice and naturally looking images

3. Overview • Introduction • Point-based rendering • Depth-of-field • Depth-of-field techniques • Our contribution: Point-based depth-of-field rendering • Basic approach • Extended method: depth-of-field with level of detail • Results • Discussion • Conclusions

4. y z x Point-based rendering • Object represented by points without connectivity • Point (surfel) • position, normal, radius, material • Rendering = screen space surface reconstruction • Efficient for very complex objects

5. Depth-of-Field • More naturally looking images • Important depth cue for perception of scene configuration • Draws attention to the focused objects

6. VD D Circle of Confusion (CoC) C C = f ( F, F/n, D, P ) F…... focal distanceF/n… lens diameterP……focal plane distanceD……point depth Thin Lens Camera Model VP P F/n image plane lens focal plane

7. Depth-of-Field Techniques in CG • Supersampling • Distributed ray tracing [Cook et al. 1984] • Sample the light paths through the lens • Multisampling [Haeberli & Akeley 1990] • Several images from different viewpoints on the lens • Average the resulting images using accumulation buffer

8. Image + depth Image with DOF Depth of Field Techniques in CG • Post-filtering [Potmesil & Chakravarty 1981] • Out-of-focus pixels displayed as CoC • Intensity leakage, hypo-intensity • Slow for larger kernels Focus processor(filtering) Image synthesizer

9. splat Point-based rendering - splatting • Draw each point as a fuzzy splat (an ellipse) Image =  SPLATi

10. Our Approach: Swap  and Focus filtering SPLATi Focus filtering  SPLATj Focus filtering Image with DOF SPLATk Focus filtering Our Basic Approach • Post-filtering Focus processor(filtering) Image with DOF i SPLATi + depth Image + depth Image =i SPLATi

11. Splat = reconstr. kernel r Blurred reconstr. kernel rDOF = r GQDOF DOF filter GQDOF Our Basic Approach

12. Properties of our basic approach PROS… + Avoids leakage • Reconstruction takes into account the splat depth + No hypo-intensities • Visibility resolved after blurring + Handles transparency • In the same way as the EWA splatting – A-buffer CONS - Very slow, especially for large apertures • A lot of large overlapping splats • High number of fragments: • E.g. Lion, no blur: 2.3 mil.; blur 90.2 mil. (40x more)

13. Our Extended Method • Use Level of Detail (LOD) to attack complexity • blur = detail • Select lower LOD for blurred parts • # of fragments increases more slowly • E.g. Lion, no blur: 2.3 mil.; blur 5.3 mil. (2.3x more) Blurred img. Selected LOD

14. Observation • Selecting lower LOD for rendering equivalent to 1) selecting the fine LOD 2) low-pass filtering is screen space • Use LOD as a means for blurring • not only to reduce complexity Fine LOD Lower LOD

15. Effect of LOD Selection • How to quantify the effect of LOD selection in terms of blur in the resulting image ? • We use Bounding sphere hierarchy • Qsplat [Rusinkiewicz & Levoy, 2000]

16. subsample Center the filter GQL Bounding Sphere Hierarchy • Building the hierarchy levels low-pass filtering + subsampling The finest level: L=0 Lower level: L=1

17. LOD Filter in Screen Space • GQL defined in local coordinates in object space • GQL related to screen space by the local affine approximation J of the object-to-screen transform • Selecting level L = filtering in screen space by GJQLJT GJQLJT GQL Screen space Object space

18. rDOF = r GQDOF y r GJQLJT x DOF with LOD - Algorithm • Given the required screen space filter GQDOF • Select LOD L such that support( GJQLJT ) < support ( r GQDOF ) • Apply an additional screen space filter GQDIFF to get GQDOF rDOF = [r GJQLJT ] GQDIFF

19. Results No Depth-of-Field – everything in focus

20. Results Transparent mask in focus, male figure out of focus

21. Results Male figure in focus, transparent mask out of focus

22. Results Reference solution (multisampling) Our algorithm • Our blur looks too smooth because of the Gaussian filter

23. Results Reference solution (multisampling) Our algorithm • Artifacts due to incorrect surface reconstruction

24. Discussion • Simplifying assumptions & limitations • Gaussian distribution of light within the CoC • Mostly ok • We are blurring the texture before lighting • We should blur after lighting • Possible incorrect image reconstruction from blurred splats

25. Conclusion • A novel algorithm for depth of field rendering • LOD as a means for depth-blurring + Transparency + Avoids intensity leakage + Running time independent of the DOF - Only for point based rendering - A number of artifacts can appear • Ideal tool for interactive DOF previewing • Trial and error camera parameters setting Acknowledgement: Grant 2159/2002 MSMT Czech Republic