2,600 research outputs found
Recognising the Clothing Categories from Free-Configuration Using Gaussian-Process-Based Interactive Perception
In this paper, we propose a Gaussian Process- based interactive perception approach for recognising highly- wrinkled clothes. We have integrated this recognition method within a clothes sorting pipeline for the pre-washing stage of an autonomous laundering process. Our approach differs from reported clothing manipulation approaches by allowing the robot to update its perception confidence via numerous interactions with the garments. The classifiers predominantly reported in clothing perception (e.g. SVM, Random Forest) studies do not provide true classification probabilities, due to their inherent structure. In contrast, probabilistic classifiers (of which the Gaussian Process is a popular example) are able to provide predictive probabilities. In our approach, we employ a multi-class Gaussian Process classification using the Laplace approximation for posterior inference and optimising hyper-parameters via marginal likelihood maximisation. Our experimental results show that our approach is able to recognise unknown garments from highly-occluded and wrinkled con- figurations and demonstrates a substantial improvement over non-interactive perception approaches
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points
Multi-view stereo (MVS) is the golden mean between the accuracy of active
depth sensing and the practicality of monocular depth estimation. Cost volume
based approaches employing 3D convolutional neural networks (CNNs) have
considerably improved the accuracy of MVS systems. However, this accuracy comes
at a high computational cost which impedes practical adoption. Distinct from
cost volume approaches, we propose an efficient depth estimation approach by
first (a) detecting and evaluating descriptors for interest points, then (b)
learning to match and triangulate a small set of interest points, and finally
(c) densifying this sparse set of 3D points using CNNs. An end-to-end network
efficiently performs all three steps within a deep learning framework and
trained with intermediate 2D image and 3D geometric supervision, along with
depth supervision. Crucially, our first step complements pose estimation using
interest point detection and descriptor learning. We demonstrate
state-of-the-art results on depth estimation with lower compute for different
scene lengths. Furthermore, our method generalizes to newer environments and
the descriptors output by our network compare favorably to strong baselines.
Code is available at https://github.com/magicleap/DELTASComment: ECCV 202
DeMoN: Depth and Motion Network for Learning Monocular Stereo
In this paper we formulate structure from motion as a learning problem. We
train a convolutional network end-to-end to compute depth and camera motion
from successive, unconstrained image pairs. The architecture is composed of
multiple stacked encoder-decoder networks, the core part being an iterative
network that is able to improve its own predictions. The network estimates not
only depth and motion, but additionally surface normals, optical flow between
the images and confidence of the matching. A crucial component of the approach
is a training loss based on spatial relative differences. Compared to
traditional two-frame structure from motion methods, results are more accurate
and more robust. In contrast to the popular depth-from-single-image networks,
DeMoN learns the concept of matching and, thus, better generalizes to
structures not seen during training.Comment: Camera ready version for CVPR 2017. Supplementary material included.
Project page:
http://lmb.informatik.uni-freiburg.de/people/ummenhof/depthmotionnet
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