8,054 research outputs found
Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images
We propose a simple and efficient method for exploiting synthetic images when
training a Deep Network to predict a 3D pose from an image. The ability of
using synthetic images for training a Deep Network is extremely valuable as it
is easy to create a virtually infinite training set made of such images, while
capturing and annotating real images can be very cumbersome. However, synthetic
images do not resemble real images exactly, and using them for training can
result in suboptimal performance. It was recently shown that for exemplar-based
approaches, it is possible to learn a mapping from the exemplar representations
of real images to the exemplar representations of synthetic images. In this
paper, we show that this approach is more general, and that a network can also
be applied after the mapping to infer a 3D pose: At run time, given a real
image of the target object, we first compute the features for the image, map
them to the feature space of synthetic images, and finally use the resulting
features as input to another network which predicts the 3D pose. Since this
network can be trained very effectively by using synthetic images, it performs
very well in practice, and inference is faster and more accurate than with an
exemplar-based approach. We demonstrate our approach on the LINEMOD dataset for
3D object pose estimation from color images, and the NYU dataset for 3D hand
pose estimation from depth maps. We show that it allows us to outperform the
state-of-the-art on both datasets.Comment: CVPR 201
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network
With more and more household objects built on planned obsolescence and
consumed by a fast-growing population, hazardous waste recycling has become a
critical challenge. Given the large variability of household waste, current
recycling platforms mostly rely on human operators to analyze the scene,
typically composed of many object instances piled up in bulk. Helping them by
robotizing the unitary extraction is a key challenge to speed up this tedious
process. Whereas supervised deep learning has proven very efficient for such
object-level scene understanding, e.g., generic object detection and
segmentation in everyday scenes, it however requires large sets of per-pixel
labeled images, that are hardly available for numerous application contexts,
including industrial robotics. We thus propose a step towards a practical
interactive application for generating an object-oriented robotic grasp,
requiring as inputs only one depth map of the scene and one user click on the
next object to extract. More precisely, we address in this paper the middle
issue of object seg-mentation in top views of piles of bulk objects given a
pixel location, namely seed, provided interactively by a human operator. We
propose a twofold framework for generating edge-driven instance segments.
First, we repurpose a state-of-the-art fully convolutional object contour
detector for seed-based instance segmentation by introducing the notion of
edge-mask duality with a novel patch-free and contour-oriented loss function.
Second, we train one model using only synthetic scenes, instead of manually
labeled training data. Our experimental results show that considering edge-mask
duality for training an encoder-decoder network, as we suggest, outperforms a
state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly
Robotics, 10th International Workshop, Springer Proceedings in Advanced
Robotics, vol 7. The final authenticated version is available online at:
https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in
Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly
Robotics, 10th International Workshop,
Hybrid One-Shot 3D Hand Pose Estimation by Exploiting Uncertainties
Model-based approaches to 3D hand tracking have been shown to perform well in
a wide range of scenarios. However, they require initialisation and cannot
recover easily from tracking failures that occur due to fast hand motions.
Data-driven approaches, on the other hand, can quickly deliver a solution, but
the results often suffer from lower accuracy or missing anatomical validity
compared to those obtained from model-based approaches. In this work we propose
a hybrid approach for hand pose estimation from a single depth image. First, a
learned regressor is employed to deliver multiple initial hypotheses for the 3D
position of each hand joint. Subsequently, the kinematic parameters of a 3D
hand model are found by deliberately exploiting the inherent uncertainty of the
inferred joint proposals. This way, the method provides anatomically valid and
accurate solutions without requiring manual initialisation or suffering from
track losses. Quantitative results on several standard datasets demonstrate
that the proposed method outperforms state-of-the-art representatives of the
model-based, data-driven and hybrid paradigms.Comment: BMVC 2015 (oral); see also
http://lrs.icg.tugraz.at/research/hybridhape
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