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Evaluating the effectiveness of representing numeric information through abstract graphics in 3D desktop virtual environments
Hybrid Bayesian Eigenobjects: Combining Linear Subspace and Deep Network Methods for 3D Robot Vision
We introduce Hybrid Bayesian Eigenobjects (HBEOs), a novel representation for
3D objects designed to allow a robot to jointly estimate the pose, class, and
full 3D geometry of a novel object observed from a single viewpoint in a single
practical framework. By combining both linear subspace methods and deep
convolutional prediction, HBEOs efficiently learn nonlinear object
representations without directly regressing into high-dimensional space. HBEOs
also remove the onerous and generally impractical necessity of input data
voxelization prior to inference. We experimentally evaluate the suitability of
HBEOs to the challenging task of joint pose, class, and shape inference on
novel objects and show that, compared to preceding work, HBEOs offer
dramatically improved performance in all three tasks along with several orders
of magnitude faster runtime performance.Comment: To appear in the International Conference on Intelligent Robots
(IROS) - Madrid, 201
Systematic model behavior of adsorption on flat surfaces
A low density film on a flat surface is described by an expansion involving
the first four virial coefficients. The first coefficient (alone) yields the
Henry's law regime, while the next three correct for the effects of
interactions. The results permit exploration of the idea of universal
adsorption behavior, which is compared with experimental data for a number of
systems
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