Video featuring Distance Fields
Hey everyone!
I am proud to present a new entry in the Demos section.
We demonstrate the Euclidean Distance Transformation feature of GPU-Voxels which allows you to derive 3D Distance Fields from pointclouds with high performance. In the video a flying drone plans a path through a environment, sensed only with on-board sensors. From the data a distance map is built on the fly with 30 Hz. So for every Voxel a simple wavefront planner can query the closest obstacle (plus its distance) and find a path that keeps a safety margin from the walls and only chooses openings that are large enough for the drone:
Try the feature by yourself! The X-Mas Release of GPU-Voxels comes with a “distance-kinect” example.
Thanks to Christian Jülg for re-implementing the “Parallel Banding Algorithm” that derives the Distance Maps! Here is a link to Cao Thanh Tung‘s original work: http://www.comp.nus.edu.sg/~tants/pba.html
Cheers,
Andreas