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

2. 2/25 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. 3/25 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. 4/25 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 x y z

5. 5/25 Depth-of-Field More naturally looking images Important depth cue for perception of scene configuration Draws attention to the focused objects

6. 6/25 Thin Lens Camera Model image planefocal planelens VPVP P F/n DVDVD C Circle of Confusion (CoC) C = f ( F, F/n, D, P ) F …... focal distance F/n … lens diameter P ……focal plane distance D ……point depth

7. 7/25 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. 8/25 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 + depth Image with DOF Image synthesizer

9. 9/25 Point-based rendering - splatting Draw each point as a fuzzy splat (an ellipse) Image =  SPLAT i splat

10. 10/25 Our Basic Approach Post-filtering Focus processor (filtering) Image + depth Image with DOF Image =  i SPLAT i  i SPLAT i + depth Our Approach: Swap  and Focus filtering Focus filtering Image with DOF SPLAT i  Focus filtering SPLAT j Focus filtering SPLAT k

11. 11/25 Our Basic Approach Splat = reconstr. kernel  DOF filter G Q DOF Blurred reconstr. kernel  DOF  G Q DOF

12. 12/25 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. 13/25 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. 14/25 Fine LODLower LOD 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

15. 15/25 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. 16/25 Bounding Sphere Hierarchy The finest level: L=0Lower level: L=1 subsample Building the hierarchy levels low-pass filtering + subsampling Center the filter G Q L

17. 17/25 LOD Filter in Screen Space G Q L defined in local coordinates in object space G Q L related to screen space by the local affine approximation J of the object-to-screen transform Selecting level L = filtering in screen space by G JQ L J T Screen space GQLGQL G JQ L J T Object space

18. 18/25 DOF with LOD - Algorithm Given the required screen space filter G Q DOF 1.Select LOD L such that support( G JQ L J T ) < support (  G Q DOF ) 2.Apply an additional screen space filter G Q DIFF to get G Q DOF  DOF  G Q DOF  DOF  G JQ L J T  G Q DIFF  G JQ L J T

19. 19/25 Results No Depth-of-Field – everything in focus

20. 20/25 Results Transparent mask in focus, male figure out of focus

21. 21/25 Results Male figure in focus, transparent mask out of focus

22. 22/25 Results Our algorithm Reference solution (multisampling) Our blur looks too smooth because of the Gaussian filter

23. 23/25 Results Our algorithm Reference solution (multisampling) Artifacts due to incorrect surface reconstruction

24. 24/25 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. 25/25 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