03 Jan, 2019
New Post on Depth Sensing from BAIR
Daniel Seita, Jeff Mahler, Mike Danielczuk, Matthew Matl, and Ken Goldberg collaborated through Berkeley Artificial Intelligence Research (BAIR) to publish their work on depth, titled Drilling Down on Depth Sensing and Deep Learning.
Their research analyzes the importance of depth images and explores "two independent innovations and the potential for combining them in robotics." Through the article, they support their research in sensing depth through examples, primarily using robot grasping, segementing objects in bins, and robot bed-making. They ultimately draw the conclusion that depth matters.
Read their full work here.
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