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GTC ON-DEMAND

Artificial Intelligence and Deep Learning
Presentation
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GPU-Accelerated Expectation Maximization for Fast Point Cloud Segmentation
Abstract:

Many modern 3D range sensors generate on the order of one million data points per second and form the foundation of many modern applications in robotic perception. For real-time performance, it is beneficial to leverage parallel hardware when possible. This poster details work to quickly compress a raw point cloud into a set of parametric surfaces using a GPU-accelerated form of Expectation Maximization. We find that our algorithm is over an order of magnitude faster than the serial C version, while the segmentation provides several orders of magnitude savings in memory while still preserving the geometric properties of the data.

 
Topics:
Artificial Intelligence and Deep Learning
Type:
Poster
Event:
GTC Silicon Valley
Year:
2014
Session ID:
P4274
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