Image Quality-Driven Level of Detail Selection on a Triangle Budget

Ludvig ArlebrinkFredrik Linde

What is Level of Detail?

Level of detail (LOD) is an optimization technique [1]

From a distance the user will not be able to tell the difference between a more or less complex triangular mesh

The most common LOD technique is discrete LOD (DLOD)

Motivation

The common criterias for DLOD is often the distance from the camera or space the object occupies on the screen

Unity’s built-in approach decides LOD version based on threshold values for screen relative heights to use for the transition [2]

Our approach uses perceptual metrics to only lower the LOD version for the objects with least impact on the image quality

Research Question

How does the SSIM quality of rendered images, from a free moving camera, compare between Unity's built-in

approach to LOD and the proposed pre-computed approach?

Framework

Create a grid with four cameras in every corner in the grid

Each camera stores LOD settings for each mesh within its view frustum

Using SSIM index [3] to determine which LOD version to be used for a given camera

Models - LOD versions and triangle count

Armadillo Dragon LucyBunny Sphere

LOD 0 LOD 1 LOD 2 LOD 3 LOD 4

Armadillo 64860 32430 16214 8106 4052

Bunny 69666 34832 17416 8708 4354

Dragon 55000 27500 13750 6874 3436

Lucy 59998 29998 17120 14452 13898

Sphere 28560 14380 7140 3570 1784

Results - Pre-Processing Time

Time Unique Meshes Cameras

Scene 1 13h 25min 5 404

Scene 2 30h 11min 10 378

Scene 3 15h 39min 25 284

Scene 1 Scene 2 Scene 3

Results - Average SSIM quality

Framework Unity

Scene 1 0.999916 0.999579

Scene 2 0.989877 0.995292

Scene 3 0.988706 0.988967

Scene 1 Scene 2 Scene 3

Unity Framework

Similarity (Index) Similarity (Index) Similarity (Index)0.9

1

0.9

1

0.9

1

Imperceivable difference example

Framework: 0.999998 Unity: 0.998946

Reference

Perceivable difference example

Framework: 0.924151 Unity: 0.989815

Reference

Results - Average perceptual pixel difference

Framework Unity

Scene 1 346 px 354 px

Scene 2 29134 px 11483 px

Scene 3 32052 px 31668 px

Scene 1 Scene 2 Scene 3

Unity Framework

0

600 000

Error (Pixels)0

600 000

0

600 000

Error (Pixels)Error (Pixels)

[4]

Analysis - Scene 1 (N, N + 1) AverageFramework

Reference

Framework

Difference

0

8000

0.9

1

Error (Pixels)

Similarity (Index)

Analysis - Scene 2 (N, N + 1) AverageFramework

Reference

Framework

Difference

0

600 000

0.9

1

Error (Pixels)

Similarity (Index)

Analysis - Scene 3 (N, N + 1) AverageFramework

Reference

Framework

Difference

Error (Pixels)

Similarity (Index)

0

600 000

0.9

1

Analysis - Pearson correlation coefficient

Pearson correlation Magnitude

Scene 1 - 0.746 86%

Scene 2 - 0.761 87%

Scene 3 - 0.854 92%

Scene 1 Scene 2 Scene 3

SSIM Quality SSIM Quality SSIM QualityPix

el D

iffer

ence

Pix

el D

iffer

ence

Pix

el D

iffer

ence

[5]

Conclusions & Future Work

● Unity's built-in approach generally performed better in terms of SSIM

● Long pre-processing time, the process could be more parallel

● Improvements to our approach could be made

○ Fine-tuning of picking reference camera

References[1] David P Luebke. Level of detail for 3D graphics. Morgan Kaufmann. 2003.

[2] Unity - Manual: LOD Group. [Online; accessed 25-May-2018].

[3] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality

assessment: from error visibility to structural similarity.

IEEE transactions on image processing, 13(4):600–612, 2004.

[4] Hector Yee. Perceptual metric for production testing.

Journal of Graphics Tools, 9(4):33–40, 2004.

[5] SPSS Tutorials: Pearson Correlation. [Online; accessed 25-May-2018]

Contact

Ludvig Arlebrinkluar15@student.bth.se

Fredrik Lindefrld15@student.bth.se