Spray Cans Are Becoming "Smart"

'Smart' Spray Can Requires No Artistic Training

Greg Watry | April 11, 2016

Street art has long been ingrained in modern culture. While graffiti artists like Banksy are popular, with their work sometimes fetching thousands of dollars at an auction, spray painting is still illegal. It’s a labor carried out under the cover of night with stencils on hand.
Now, robots are getting into the spray paint game.
Researchers from ETH Zurich, Disney Research Zurich, Dartmouth College, and Columbia University have developed a “smart” spray can capable of painting murals all on its own.

"This spray-painted tiger was created using only four colors of spray pain." Source: http://www.gizmag.com/smart-spray-paint-computer-aided-mural-dartmouth/42715/ (See video at this site.)

<more at http://www.rdmag.com/articles/2016/04/smart-spray-can-requires-no-artistic-training; related articles and links: http://www.gizmag.com/smart-spray-paint-computer-aided-mural-dartmouth/42715/ (+Video) (Smart spray paint copies color photos onto walls. April 8, 2016) and https://www.cs.dartmouth.edu/~wjarosz/publications/prevost16large.pdf (Large-Scale Painting of Photographs by Interactive Optimization. Romain Prévost, Alec Jacobson, Wojciech Jarosz, Olga Sorkine-Hornung. Article accepted to Computers & Graphics – doi:10.1016/j.cag.2015.11.001, November 4, 2015. [Abstract: We propose a system for painting large-scale murals of arbitrary input photographs. To that end, we choose spray paint, which is easy to use and affordable, yet requires skill to create interesting murals. An untrained user simply waves a programmatically actuated spray can in front of the canvas. Our system tracks the can’s position and determines the optimal amount of paint to disperse to best approximate the input image. We accurately calibrate our spray paint simulation model in a pre-process and devise optimization routines for run-time paint dispersal decisions. Our setup is light-weight: it includes two webcams and QR-coded cubes for tracking, and a small actuation device for the spray can, attached via a 3D-printed mount. The system performs at haptic rates, which allows the user – informed by a visualization of the image residual – to guide the system interactively to recover low frequency features. We validate our pipeline for a variety of grayscale and color input images and present results in simulation and physically realized murals.])>