4,426 research outputs found
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
A fast 3-D object recognition algorithm for the vision system of a special-purpose dexterous manipulator
A fast 3-D object recognition algorithm that can be used as a quick-look subsystem to the vision system for the Special-Purpose Dexterous Manipulator (SPDM) is described. Global features that can be easily computed from range data are used to characterize the images of a viewer-centered model of an object. This algorithm will speed up the processing by eliminating the low level processing whenever possible. It may identify the object, reject a set of bad data in the early stage, or create a better environment for a more powerful algorithm to carry the work further
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