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Exploiting Temporal Coherence in Ray Casted Walkthrougs. Vlastimil Havran , Jiří Bittner and Hans-Peter Seidel. AG4, MPI Informatik, Saarbruecken, Germany. Institute for Computer Graphics, Vienna University of Technology, Austria. Talk Outline. Introduction

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slide1

Exploiting Temporal Coherence

in Ray Casted Walkthrougs

Vlastimil Havran, Jiří Bittner

and Hans-Peter Seidel

AG4, MPI Informatik,

Saarbruecken, Germany

Institute for Computer Graphics, Vienna University of Technology, Austria

slide2

Talk Outline

Introduction

– ray shooting and ray casting

– temporal coherence

New Algorithm

Results and Demo

Conclusions and Future Work

slide3

B

A

C

ray

D

Introduction: Ray Shooting

Task: Given a ray, find out

the first object intersected.

Input: a scene and a ray

Output: the object C

and

signed distance

slide4

Ray Casting

Image pixel

rays

slide5

Temporal Coherence

“Weak Definition”: a similarity between subsequent images in the animation.

Exploiting temporal coherence: speed up computation.

Algorithm Categories

A – static camera, moving objects

B – moving camera, static objects (walkthrough)

C – moving camera, moving objects

Many coherence classes exist.

– ray coherence, spatial coherence, temporal coherence, traversal coherence, etc.

(see Eduard Groeller’s PhD thesis, TU Vienna)

slide6

Previous and Related Work

Glassner, 88: Spacetime ray tracing for animation.

Badt Jr, 88: Two algorithms for taking advantage of

temporal coherence in ray tracing.

Groeller and Purgathofer, 91: Using Temporal and spatial

coherence for acceleration of animation sequences.

Sudarsky, 93: Exploiting Temporal Coherence in

Animation Rendering. A Survey.

Adelson, 95: Generating exact-raytraced animation frames

by reprojection.

Walter et al., 99: Interactive rendering using Render Cache,

EGWR’99.

Reinhard et al., 01: Parallel Point Reprojection.

Lin. Q et al., 00: Frame Coherent Volume Rendering.

slide7

New Algorithm: Overview

Main Idea:

- Compute a single intersection of the ray with an

object, if possible

- If not possible -> ordinary ray shooting algorithm

First frame: use ordinary ray shooting, remember

intersection points in 3D space

Next frames: reproject points, check if we can

decide on intersection objects

Properties:

intersection points and surface normals compute

correctly (unlike in RenderCache etc.)

slide8

New Algorithm: Data Structures

POS = array in 2D {point in 3D, object ID, distance}

(Points in Object Space)

AIP = array in 2D {object, distance, count}

(Auxiliary Image Plane)

Size of AIP and POS = width * height

First frame: use an ordinary ray shooting algorithm,

remember the points where ray hit objects

(store it to an array POS)

slide9

Reprojection phase

“Next frame(s) – using reprojection”:

for each pixel (xx,yy) do

– reproject a point (POS(xx,yy)) from previous frame to AIP at pixel (x,y), compute approximate distance tp.

– for each reprojected point from POS store distance tp into neighbourhood 3x3 of (x,y) at AIP. Overwrite farther already stored points by closer ones. If the reprojected points are from the same object, increment AIP(X,Y).count, for all 9 pixels X=x-1,x,x+1, Y=y-1,y,y+1.

end-for

slide10

Ray Casting based on Reprojection

“Next frame(s) – ray casting”:

for each pixel (x,y) (x=0...width), (y=0...height) do

if AIP(x,y).count > THRESHOLD then

ray-cast ray(x,y) to AIP(x,y).OBJ

end-if

if (intersection was not found) then

use an ordinary ray shooting algorithm.

end-if

end-for THRESHOLD = 5, 6, 7, 8, or 9

slide11

Dilatation of Objects (footprint)

Properties: reprojection using 3x3 neighbourhood corresponds to dilatation on the image plane.

slide12

Scene Example

RED - reprojection

successfull

GREEN - reprojection

fails

WHITE - regular

resampling

slide13

Reprojection Failures

Question: When reprojection cannot be used ?

Answer: appearance of new objects occluding

previously visible objects

a) that were outside viewing frustum

(view frustum errorr)

b) that were occluded

(occlusion error)

c) that were too small

(undersampling error)

slide14

View Frustum Error

Moving backward.

View Frustum Error

slide15

Occlusion Error

Occluded objects that appear.

Occlusion Error

slide17

Correct Reprojection Algorithm

Check possible occlusion on the fly.

slide18

Small Approaching Objects

Problem: Let us have a scene with polygon A that in reprojection has big footprint. Some small objects of current subpixel size are placed in front of A and are not currently visible.

Question: What happens when we enclose the polygon and use only and only the reprojection ?

Answer: Small objects are missing!

slide19

Resampling Order

– maximize the probability that small object of subpixel size

will be detected when moving camera straight forward.

Goal:

minimize discrepancy

of resampling pattern

in spatio-temporal domain

slide20

Further Improvements

Shifting Ray Origin - when reprojection fails, we can move the ray origin along the ray path to avoid traversing empty space.

Handling background - sphere enclosing the whole scene, use as any other object (always overwritten).

Double Reprojection - edges between connected objects in 3D must be recomputed. Use two AIP arrays, keep two closest objects information.

Collision detection - between camera and the environments based on signed distance is quite simple to implement.

slide21

Note on Reprojection Efficiency

How many arithmetical operations to reproject one

point from 3D space to image plane ?

8x (+), 3x (-), 12x (multiplication), 1x (division), 1xSQRT, and 2xIF.

Note: Image-based incremental reprojection techniques by McMillan and Mark are not applicable, since we need signed distance!

slide23

Results: HW independent profiling

ORSA: ordinary ray shooting algorithm

REPR: ray casting with reprojection

IRSA: ideal ray shooting algorithm

N_IT

FPS

N_TS

T_REN

ORSA 9.23 52.0 701 sec 1.17

Scene A, 85k objects,

825 frames

REPR 3.37 12.2 478 sec 1.72

IRSA 0.98 0.0 256 sec 3.22

ORSA 3.02 44.5 892 sec 1.65

Scene B, 626k objects,

1489 frames

REPR 1.89 21.9 789 sec 1.89

IRSA 0.98 0.0 355 sec 4.19

ORSA 3.79 58.1 979 sec 1.19

Scene C, 112k objects,

1165 frames

REPR 2.73 26.0 838 sec 1.39

IRSA 0.999 0.0 374 sec 3.11

slide24

Results Summary

– tested on three different scenes.

– increased speedup (with shading, 512x512):

scene A – 1.17 fps to 1.72 fps (85x10^3 objects)

scene B – 1.65 fps to 1.89 fps (626x10^3 objects)

scene C – 1.19 fps to 1.39 fps (112x10^3 objects)

– 11.1% pixels computed by regular resampling

– 78.9% pixels computed by reprojection

– 10% pixels computed by ordinary ray shooting algorithm

Question: is this interesting and valuable speedup ?

slide25

Results: Profiling (scene A)

ORSA REPR IRSA

RayShooting Function 63.4% 25.5% 18.7%

Compute Color 28.7% 41.4% 63.6% 281 sec

Ray Initialization 1.1% 1.7% 2.9%

Extra ray-object inters. ------ 3.2% ------

Reprojection phase ------ 14.7% ------

Rest of Computation 6.7% 13.5% 15.3%

Profiler timing [sec] 1001 676 442

REPR/ORSA: Visibility speedup = 1.81 (45% savings in time)

IRSA/ORSA: Visibility speedup = 4.44 (77% savings in time) !!!

slide26

Conclusions

– conservative ray casting algorithm based on temporal coherence using reprojection aimed at solving hidden surface removal (visibility).

(previous algorithms based on reprojection interpolate colour information from 3D space).

– possible use in online mode (many reprojection techniques for ray-casting/tracing are offline).

– good use of temporal coherence, where temporal coherence exists (80% pixels computed by single ray-object intersection).

slide27

Future Work

– use in context of global illumination methods.

– generalisation to scenes with moving objects.

– instead of using objects ID, use a pointer to the

cells of spatial subdivision, thus decreasing the

dependency on visual complexity.

– extension to visibility for direct illumination

is possible (point light source exactly and

area light sources with good quality).