Splatting Jian Huang, CS 594, Spring 2002 This set of slides reference slides made by Ohio State...

Splatting

Jian Huang, CS 594, Spring 2002

This set of slides reference slides made by Ohio State University alumuni over the past several years.

Volumetric Ray Integration

color

opacity

object (color, opacity)

1.0

Splatting• Lee Westover - Vis 1989; SIGGRAPH 1990

• Object order method

• Front-To-Back or Back-To-Front

• Original method - fast, poor quality

• Many many improvements since then!– Crawfis’93: textured splats

– Swan’96, Mueller’97: anti-aliasing

– Mueller’98: image-aligned sheet-based splatting

– Mueller’99: post-classified splatting

– Huang’00: new splat primitive: FastSplats

Splatting

• Volume = field of 3D interpolation kernel– One kernel at each grid voxel

• Each kernel leaves a 2D footprint on screen– Voxel contribution = footprint ·(C, opacity)

• Weighted footprints accumulate into image

voxel kernels screen footprints = splats

screen

Splatting





screen

Splatting





screen

Splatting





screen

Ray-casting - revisited

color c = c s s(1 - ) + c

opacity = s (1 - ) +

1.0

object (color, opacity)

volumetric compositingInterpolationkernel

Ray-casting - revisited

• (ideally) we would reconstruct the continuous volume (cloud) using the interpolation kernel h:

• the we would compute the analytic integral along a ray r:

• this can only be approximated by discretization

k

kkr drvfvrphdrrpfpI )()(

k

kkr vfvvhvf )()()(

(hey! Which optical model is this equation??)

Splatting - principal idea

• This last equation

• can be rewritten in the following way:

k

kk drvfvrphpI )(

k

kk drvrphvfpI )(

Splatting Kernel or “Splat” Which can be computed analyticallyanalytically: known as footprint

dzzyxhyx ,,),(Splat

Footprint Extent

Approximate the 3D kernel (h(x,y,z))extent by a sphere

Footprint Table

A popular kernel is a three-dimensional Gaussian (radially symmetric)

As 1D integration of 3D Gaussian is still a 2D Gaussian – we can just skip the Z integration and evaluate the Gaussianfunction on 2D image space after voxel projection

Generic footprinttable

preprocessing

View-dependent footprint

It is possible to transform a sphere kernel into A ellipsoid

• The projection of an ellipsoid is an ellipse

• We need to transform the generic footprint table to the ellipse

View-dependent footprint (2)

Example Footprint at Different Resolutions

Footprint - principal idea

• Draw each voxel as a cloud of points (footprint) that spreads the voxel contribution across multiple pixels.

• Larger footprint -> larger spatial kernel extent -> lower frequency components -> more blurring– Large pixel/voxel ratio

Rendering a Splat• Use texture mapping hardware to resample footprint

table (either single density channel or separate classified r,g,b,a channels)

• Or, use FastSplats to render each splat as a graphics primitive of itself

Splatting - efficiency

• “footprint” - splatted (integrated) kernel

• if interpolation kernel is isotropic (spherical) then its footprint is independent of the view point (for orthographic viewing)

• for perspective - footprint can be approximated with an ellipse

• Hence, for common cases, we can pre-integrate it (efficient!)

• for perspective projection, to approximate, we have to compute the orientation of the ellipse

Splatting - Highlights

• Footprints can be pre-integrated– fast voxel projection

• Advantages over ray-casting:– Fast: voxel interpolation is in 2D on screen

– More accurate integration (analytic for X-ray)

– More accurate reconstruction (afford better kernels)

– Only relevant voxels must be projected

Early Implementation – Axis Aligned Splatting

• Voxel kernels are added within axis-aligned sheets• Sheets are composited front-to-back• Sheets = volume slices most perpendicular to the

image plane

image plane at 70° image plane at 30°

volume slices

x

yz

volume slices


sheet buffer

compositing buffer

volume slices

image plane

• Volume


sheet buffer

compositing buffer

volume slices

image plane

• Add voxel kernels within first sheet


sheet buffer

compositing buffer

volume slices

image plane

• Transfer to compositing buffer


sheet buffer

compositing buffer

volume slices

image plane

• Add voxel kernels within second sheet


sheet buffer

compositing buffer

volume slices

image plane

• Composite sheet with compositing buffer


sheet buffer

compositing buffer

volume slices

image plane

• Add voxel kernels within third sheet


sheet buffer

compositing buffer

volume slices

image plane

• Composite sheet with compositing buffer

What Doesn’t Work?• Mathematically, the early splatting methods only work for X-ray type

of rendering, where voxel ordering is not important– Bad approximation for other types of optical models

• Object ordering is important in volume rendering, front objects hide back objects– need to composite splats in proper order, else we get bleeding

of background objects into the image (color bleeding!)

• Axis- aligned approach add all splats that fall within a volume slice most parallel to the image plane, composite these sheets in front- to- back order– Incorrect accumulating on axis-aligned face cause popping

• A better approximation with Riemann sum is to use the image-aligned sheet-based approach

Problems Early Implementation – Axis Aligned Splatting

• In-accurate compositing, result in color bleeding and popping artifacts (Demo)!

Part of this voxel

gets composited beforepart of this voxel

Image-Aligned Sheet-Buffer

sheet buffer

compositing buffer

• Slicing slab cuts kernels into sections

• Kernel sections are added into sheet-buffer

• Sheet-buffers are composited

image plane


sheet buffer

compositing buffer




image plane


sheet buffer

compositing buffer




image plane


sheet buffer

compositing buffer




image plane


sheet buffer

compositing buffer




image plane


sheet buffer

compositing buffer




image plane


sheet buffer

compositing buffer




image plane

Image-Aligned Splatting• Note: We need an array of footprint tables now. A

separate footprint table for each slice of the 3D reconstruction kernel.

Volume Rendering Pipeline: Two Variations

Volume Rendering Pipeline: Two Variations

IASB Splatting

• No popping or color bleeding• Sharp, noise-free images

Occlusion Culling

• A voxel is only visible if the volume material in front is not opaque

occluded voxel: does not pass visibility test

wall of occluding voxels

occlusion map = opacity image

screen

Visibility Test Based on SAT of Occlusion Buffer

opacity threshold

occlusion map

Do not project Project

opacity < threshold

opacity = 0

• Compute occlusion map after each sheet• Cull both individual voxel and voxel sets with a

summed area table of occlusion map

Occlusion Culling

• Build a summed area table (SAT) from the opacity buffer

• To test whether a rectangular region is opaque or not, check the four corners

• Can cull voxel sets directly

Anti-aliasing• Needed to preserve small features• Needed for the diverging rays in perspective• In splatting, resize the footprint according to depth

Aliased anti-aliased

Motion Blur• Stretch the reconstruction kernel in the direction

of movement.• Stretch the splat footprint in the direction of the

projected movement (2D).

Camera Depth-of-Field• Two possible approaches:

– Low-pass filter the splats

– Low-pass filter the sheets

Plain with Depth-of-Field

Procedural Textures• Easily done with pre-coloring• Per-pixel

Bump-Mapping

• If calculating the normal per-pixel, we can modulate it to achieve bump mapping.

Per-pixel Classification

• Per-pixel classification can be based on gray scale, position, gradient, or ...

7.25 sec 9.41 sec (procedural) 7.99 sec

Scan-Converting A Splat

• Scan-convert an arbitrary-size radially symmetric 2D function centered at arbitrary position– circular or elliptical

• Texture mapping hardware is not the solution

• We want a hardware accelerated splat or point primitive

Fast Splats FastSplat

• We desire– fast scan conversion

– minimum or controllable errors

– compact storage

– simple integer operation

FastSplats

1D Linear 1D Squared 2D 1D with Radius Look Up Table (RLUT)

1D Linear, 1D Squared FastSplats

• On the radial line

1D Linear FastSplat, indexed by rx,y

1D Squared FastSplat, indexed by r2x,y

(x,y) (x+1,y)

rx,y

(xo,yo)

1D Squared FastSplat (Elliptical)

• For elliptical kernels, if we define a canonical radius:

• The incremental scan-conversion still works at the same low cost

FastSplats

1D Linear 1D Squared 2D

• 1D with Radius Look Up Table (RLUT)

2D FastSplat (BitBlt,VoxBlt)

• No run-time computationPre-rasterized footprint with center at (xo,yo), radius r

Pre-rasterized footprints for all possible center positions, radius r

Snap splat center to a k by k sub-pixel grid

2D FastSplats

• No run-time computation

Pre-rasterized footprints for all possible center positions, for all possible radii

Snap splat center to a k by k sub-pixel grid

Allow for a set of radius values

2D FastSplats (2)

• The storage need:

• When storage is limited, the quality is limited too

• [Mora’00], similar

FastSplats

1D Linear 1D Squared 2D

• 1D with Radius Look Up Table (RLUT)

1D RLUT FastSplat

• For hardware, we need finer parallelism than scan-line

1D FastSplat with RLUT

RLUT


• At a k by k subpixel precision
































• k by k subpixel precision


• Due to symmetry, the RLUT set for x component is the same as the RLUT set for the y component

• one RLUT set– k 1D tables– each of splat_extent length

x or y offset

k

splat_extent

k

k

Comparisons Among the FastSplats

• 1D Linear: very accurate, compact, slow

• 1D Squared: accurate, compact, fast

• 1D RLUT: accurate, compact, intended for hardware

• 2D FastSplat: can be very fast, accurate and compact under constrained conditions

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Splatting Jian Huang, CS 594, Spring 2002 This set of slides reference slides made by Ohio State...

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