222,444 research outputs found
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
We propose a Convolutional Neural Network (CNN)-based model "RotationNet,"
which takes multi-view images of an object as input and jointly estimates its
pose and object category. Unlike previous approaches that use known viewpoint
labels for training, our method treats the viewpoint labels as latent
variables, which are learned in an unsupervised manner during the training
using an unaligned object dataset. RotationNet is designed to use only a
partial set of multi-view images for inference, and this property makes it
useful in practical scenarios where only partial views are available. Moreover,
our pose alignment strategy enables one to obtain view-specific feature
representations shared across classes, which is important to maintain high
accuracy in both object categorization and pose estimation. Effectiveness of
RotationNet is demonstrated by its superior performance to the state-of-the-art
methods of 3D object classification on 10- and 40-class ModelNet datasets. We
also show that RotationNet, even trained without known poses, achieves the
state-of-the-art performance on an object pose estimation dataset. The code is
available on https://github.com/kanezaki/rotationnetComment: 24 pages, 23 figures. Accepted to CVPR 201
Learning viewpoint invariant perceptual representations from cluttered images
In order to perform object recognition, it is necessary to form perceptual representations that are sufficiently specific to distinguish between objects, but that are also sufficiently flexible to generalize across changes in location, rotation, and scale. A standard method for learning perceptual representations that are invariant to viewpoint is to form temporal associations across image sequences showing object transformations. However, this method requires that individual stimuli be presented in isolation and is therefore unlikely to succeed in real-world applications where multiple objects can co-occur in the visual input. This paper proposes a simple modification to the learning method that can overcome this limitation and results in more robust learning of invariant representations
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
We propose a real-time RGB-based pipeline for object detection and 6D pose
estimation. Our novel 3D orientation estimation is based on a variant of the
Denoising Autoencoder that is trained on simulated views of a 3D model using
Domain Randomization. This so-called Augmented Autoencoder has several
advantages over existing methods: It does not require real, pose-annotated
training data, generalizes to various test sensors and inherently handles
object and view symmetries. Instead of learning an explicit mapping from input
images to object poses, it provides an implicit representation of object
orientations defined by samples in a latent space. Our pipeline achieves
state-of-the-art performance on the T-LESS dataset both in the RGB and RGB-D
domain. We also evaluate on the LineMOD dataset where we can compete with other
synthetically trained approaches. We further increase performance by correcting
3D orientation estimates to account for perspective errors when the object
deviates from the image center and show extended results.Comment: Code available at: https://github.com/DLR-RM/AugmentedAutoencode
Deep Reflectance Maps
Undoing the image formation process and therefore decomposing appearance into
its intrinsic properties is a challenging task due to the under-constraint
nature of this inverse problem. While significant progress has been made on
inferring shape, materials and illumination from images only, progress in an
unconstrained setting is still limited. We propose a convolutional neural
architecture to estimate reflectance maps of specular materials in natural
lighting conditions. We achieve this in an end-to-end learning formulation that
directly predicts a reflectance map from the image itself. We show how to
improve estimates by facilitating additional supervision in an indirect scheme
that first predicts surface orientation and afterwards predicts the reflectance
map by a learning-based sparse data interpolation.
In order to analyze performance on this difficult task, we propose a new
challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg)
using both synthetic and real images. Furthermore, we show the application of
our method to a range of image-based editing tasks on real images.Comment: project page: http://homes.esat.kuleuven.be/~krematas/DRM
SA-Net: Deep Neural Network for Robot Trajectory Recognition from RGB-D Streams
Learning from demonstration (LfD) and imitation learning offer new paradigms
for transferring task behavior to robots. A class of methods that enable such
online learning require the robot to observe the task being performed and
decompose the sensed streaming data into sequences of state-action pairs, which
are then input to the methods. Thus, recognizing the state-action pairs
correctly and quickly in sensed data is a crucial prerequisite for these
methods. We present SA-Net a deep neural network architecture that recognizes
state-action pairs from RGB-D data streams. SA-Net performed well in two
diverse robotic applications of LfD -- one involving mobile ground robots and
another involving a robotic manipulator -- which demonstrates that the
architecture generalizes well to differing contexts. Comprehensive evaluations
including deployment on a physical robot show that \sanet{} significantly
improves on the accuracy of the previous method that utilizes traditional image
processing and segmentation.Comment: (in press
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