1,924 research outputs found
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects
We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e.
translation and rotation, of texture-less rigid objects. The dataset features
thirty industry-relevant objects with no significant texture and no
discriminative color or reflectance properties. The objects exhibit symmetries
and mutual similarities in shape and/or size. Compared to other datasets, a
unique property is that some of the objects are parts of others. The dataset
includes training and test images that were captured with three synchronized
sensors, specifically a structured-light and a time-of-flight RGB-D sensor and
a high-resolution RGB camera. There are approximately 39K training and 10K test
images from each sensor. Additionally, two types of 3D models are provided for
each object, i.e. a manually created CAD model and a semi-automatically
reconstructed one. Training images depict individual objects against a black
background. Test images originate from twenty test scenes having varying
complexity, which increases from simple scenes with several isolated objects to
very challenging ones with multiple instances of several objects and with a
high amount of clutter and occlusion. The images were captured from a
systematically sampled view sphere around the object/scene, and are annotated
with accurate ground truth 6D poses of all modeled objects. Initial evaluation
results indicate that the state of the art in 6D object pose estimation has
ample room for improvement, especially in difficult cases with significant
occlusion. The T-LESS dataset is available online at cmp.felk.cvut.cz/t-less.Comment: WACV 201
Real-Time Seamless Single Shot 6D Object Pose Prediction
We propose a single-shot approach for simultaneously detecting an object in
an RGB image and predicting its 6D pose without requiring multiple stages or
having to examine multiple hypotheses. Unlike a recently proposed single-shot
technique for this task (Kehl et al., ICCV'17) that only predicts an
approximate 6D pose that must then be refined, ours is accurate enough not to
require additional post-processing. As a result, it is much faster - 50 fps on
a Titan X (Pascal) GPU - and more suitable for real-time processing. The key
component of our method is a new CNN architecture inspired by the YOLO network
design that directly predicts the 2D image locations of the projected vertices
of the object's 3D bounding box. The object's 6D pose is then estimated using a
PnP algorithm.
For single object and multiple object pose estimation on the LINEMOD and
OCCLUSION datasets, our approach substantially outperforms other recent
CNN-based approaches when they are all used without post-processing. During
post-processing, a pose refinement step can be used to boost the accuracy of
the existing methods, but at 10 fps or less, they are much slower than our
method.Comment: CVPR 201
Photorealistic retrieval of occluded facial information using a performance-driven face model
Facial occlusions can cause both human observers and computer algorithms
to fail in a variety of important tasks such as facial action analysis and
expression classification. This is because the missing information is not
reconstructed accurately enough for the purpose of the task in hand. Most
current computer methods that are used to tackle this problem implement
complex three-dimensional polygonal face models that are generally timeconsuming
to produce and unsuitable for photorealistic reconstruction of
missing facial features and behaviour.
In this thesis, an image-based approach is adopted to solve the occlusion
problem. A dynamic computer model of the face is used to retrieve the
occluded facial information from the driver faces. The model consists of a
set of orthogonal basis actions obtained by application of principal
component analysis (PCA) on image changes and motion fields extracted
from a sequence of natural facial motion (Cowe 2003). Examples of
occlusion affected facial behaviour can then be projected onto the model to
compute coefficients of the basis actions and thus produce photorealistic
performance-driven animations.
Visual inspection shows that the PCA face model recovers aspects of
expressions in those areas occluded in the driver sequence, but the expression is generally muted. To further investigate this finding, a database
of test sequences affected by a considerable set of artificial and natural
occlusions is created. A number of suitable metrics is developed to measure
the accuracy of the reconstructions. Regions of the face that are most
important for performance-driven mimicry and that seem to carry the best
information about global facial configurations are revealed using Bubbles,
thus in effect identifying facial areas that are most sensitive to occlusions.
Recovery of occluded facial information is enhanced by applying an
appropriate scaling factor to the respective coefficients of the basis actions
obtained by PCA. This method improves the reconstruction of the facial
actions emanating from the occluded areas of the face. However, due to the
fact that PCA produces bases that encode composite, correlated actions,
such an enhancement also tends to affect actions in non-occluded areas of
the face. To avoid this, more localised controls for facial actions are
produced using independent component analysis (ICA). Simple projection
of the data onto an ICA model is not viable due to the non-orthogonality of
the extracted bases. Thus occlusion-affected mimicry is first generated using
the PCA model and then enhanced by accordingly manipulating the
independent components that are subsequently extracted from the mimicry.
This combination of methods yields significant improvements and results in
photorealistic reconstructions of occluded facial actions
Optimization for Deep Learning Systems Applied to Computer Vision
149 p.Since the DL revolution and especially over the last years (2010-2022), DNNs have become an essentialpart of the CV field, and they are present in all its sub-fields (video-surveillance, industrialmanufacturing, autonomous driving, ...) and in almost every new state-of-the-art application that isdeveloped. However, DNNs are very complex and the architecture needs to be carefully selected andadapted in order to maximize its efficiency. In many cases, networks are not specifically designed for theconsidered use case, they are simply recycled from other applications and slightly adapted, without takinginto account the particularities of the use case or the interaction with the rest of the system components,which usually results in a performance drop.This research work aims at providing knowledge and tools for the optimization of systems based on DeepLearning applied to different real use cases within the field of Computer Vision, in order to maximizetheir effectiveness and efficiency
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