Here’s Pittsburgh’s Lisa Ramsey in front of a mural by Pittsburgh’s Matt Gondek.
Lisa is a Bartkira contributor who did the Bootleg Bart music video for Girl Talk I posted earlier today.
One state away in Virginia, we have our first East Coast Bartkira show happening next month! (I know nothing about American geography. That’s probably like talking about England and then saying ‘and just one country away, in Scotland…’)
in another dimension, i would have done something like this a few years back
Scape House FORM | Kouichi Kimura Architects
"The house is located in the tiered-developed residential area on a hill. From the site, the beautiful view of the lake can be seen.The client requested that the view be fully utilized and that the space be open. In this project, versatile spaces that incorporate light and scenery were located by the windows in order to bring out the best in this house."
i don’t desire many things, but this is amazing.
i think this person is a wizard
when you don’t compete in the olympics because you want it to be fair
I don’t think Aang is the last airbender.
i’m in love with how the “flip at your own risk” sign pans in dramatically and he does fifty flips in midair right in front of it and sticks the landing pose like “go fuck yourself i do what i want”
all the awards for that comment
simple and beautiful, mang
MAP Visibility Estimation for Large-Scale Dynamic 3D Reconstruction
Interesting development for 3D video: a team at Carnegie Mellon University have developed a method of video photogrammetry to capture 3D motion, using a spherical array of video cameras at various angles within a space entitled ‘The Panoptic Studio’ - video embedded below:
Many traditional challenges in reconstructing 3D motion, such as matching across wide baselines and handling occlusion, reduce in significance as the number of unique viewpoints increases. However, to obtain this benefit, a new challenge arises: estimating precisely which cameras observe which points at each instant in time. We present a maximum a posteriori (MAP) estimate of the time-varying visibility of the target points to reconstruct the 3D motion of an event from a large number of cameras. Our algorithm takes, as input, camera poses and image sequences, and outputs the time-varying set of the cameras in which a target patch is visible and its reconstructed trajectory. We model visibility estimation as a MAP estimate by incorporating various cues including photometric consistency, motion consistency, and geometric consistency, in conjunction with a prior that rewards consistent visibilities in proximal cameras. An optimal estimate of visibility is obtained by finding the minimum cut of a capacitated graph over cameras. We demonstrate that our method estimates visibility with greater accuracy, and increases tracking performance producing longer trajectories, at more locations, and at higher accuracies than methods that ignore visibility or use photometric consistency alone.
i have no idea what’s going on