Multi-Class Blue Noise Sampling Li-Yi Wei Microsoft Research

smart↑ → research↑ Q: not so smart?

Blue noise random & uniform sample spacing r

r = 0.02 r = single classmulti class

Multi-class blue noise class 0class 1total set

Object placement

Sensor layout cone/rod cellsRGB sensors

Color stippling RGBCMYB dots

A: smart↓ → popular↑ simpler algorithms less intimidating 5456 too smart # sig10 paper

50 seconds old version

smart↑ → research↑ Q: not so smart?

Blue noise random & uniform sample spacing r

r = 0.02 r = single classmulti class

Multi-class blue noise class 0class 1total set

Object placement

Sensor layout cone/rod cellsRGB sensors

Color stippling RGBCMYB dots

A: smart↓ → popular↑ simpler algorithms less intimidating 5456 too smart # sig10 paper

Old Version continue joke from FF 2008

Blue noise distribution random & uniform dart throwing Lloyd relaxation

X not novel [fast-forward SIG 2008] X not very useful texture synthesis inverse texture synthesis flip

overlay class 0class 1 X not uniform total set

Multi-class blue noise class 0class 1total set

Object placement

Sensor layout cone/rod cellsRGB sensors

Color stippling RGBCMYB dots

Backup

overlay X not uniform

Sensor layout continuous domaindiscrete domain retina

texture synthesis

inverse texture synthesis

Old slide from SIGGRAPH 2000 input (small) output (large) texture synthesis

New SIGGRAPH 2008 paper (just by flipping the old slide) input (large) output (small) inverse texture synthesis

Object placement Uniform distribution Red flower Yellow flower Entire set

Uniform per class class 0 Poisson disk class 1 Poisson disk total set OOX

Uniform total set total set Poisson disk class 0class 1 XXO

Our method class 0 Poisson disk class 1 Poisson disk total set Poisson disk OOO