Velodyne LiDAR Announces ULTRA Puck™ VLP-32A, High Definition Real-Time 3D LiDAR Sensor for the Automotive Market
Kirsten Korosec for Fortune: Toyota will expand the footprint of its artificial intelligence and robotics research center by adding a third facility in Ann Arbor, Mich.
Gill Pratt, CEO of the Toyota Research Institute, made the announcement on Thursday during his keynote speech at Nvidia’s GPU Technology Conference in San Jose. The Ann Arbor facility will be located near the University of Michigan, where it will fund research in artificial intelligence, robotics, and materials science.
Last year, the world’s largest automaker said it would invest $1 billion over the next five years in a research center for artificial intelligence to be based in Palo Alto, Calif. The institute aims to bridge the gap between research in AI and robotics in order to bring this technology to market. The technology is largely being developed for self-driving cars, but the institute is also researching and developing AI products for the home. Cont'd...
DAIHEN Licenses WiTricity Magnetic Resonance Technology for Wireless Charging of Automatic Guided Vehicles (AGVs)
XPONENTIAL - UAS Magazine Announces UAS Energy In Sight Summit Co-Located at AUVSI's 2016 Xponential in New Orleans
Anything Technologies Media Inc., Completes Acquisition of GSP an Innovative Technology Company focused on the Development of Drones
IEEE Standards Association Introduces Global Initiative for Ethical Considerations in the Design of Autonomous Systems
The New Mobile App from Festo Enables Fast Delivery of Parts, Greater Productivity, and Lower Inventory Cost
Efficient 3D Object Segmentation from Densely Sampled Light Fields with Applications to 3D Reconstruction
From Kaan Yücer, Alexander Sorkine-Hornung, Oliver Wang, Olga Sorkine-Hornung:
Precise object segmentation in image data is a fundamental problem with various applications, including 3D object reconstruction. We present an efficient algorithm to automatically segment a static foreground object from highly cluttered background in light fields. A key insight and contribution of our paper is that a significant increase of the available input data can enable the design of novel, highly efficient approaches. In particular, the central idea of our method is to exploit high spatio-angular sampling on the order of thousands of input frames, e.g. captured as a hand-held video, such that new structures are revealed due to the increased coherence in the data. We first show how purely local gradient information contained in slices of such a dense light field can be combined with information about the camera trajectory to make efficient estimates of the foreground and background. These estimates are then propagated to textureless regions using edge-aware filtering in the epipolar volume. Finally, we enforce global consistency in a gathering step to derive a precise object segmentation both in 2D and 3D space, which captures fine geometric details even in very cluttered scenes. The design of each of these steps is motivated by efficiency and scalability, allowing us to handle large, real-world video datasets on a standard desktop computer... (paper)
Rising Media to hold the 3rd annual 'Inside 3D Printing Conference & Expo,' Asia's largest 3D Printing Tradeshow on June 22-24, 2016 in Seoul
Endeavor Robotics the Industry's Leading Ground Robot Company Goes Private to Capitalize on Emerging Opportunities
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