22,621 research outputs found
Fast Single Shot Detection and Pose Estimation
For applications in navigation and robotics, estimating the 3D pose of
objects is as important as detection. Many approaches to pose estimation rely
on detecting or tracking parts or keypoints [11, 21]. In this paper we build on
a recent state-of-the-art convolutional network for slidingwindow detection
[10] to provide detection and rough pose estimation in a single shot, without
intermediate stages of detecting parts or initial bounding boxes. While not the
first system to treat pose estimation as a categorization problem, this is the
first attempt to combine detection and pose estimation at the same level using
a deep learning approach. The key to the architecture is a deep convolutional
network where scores for the presence of an object category, the offset for its
location, and the approximate pose are all estimated on a regular grid of
locations in the image. The resulting system is as accurate as recent work on
pose estimation (42.4% 8 View mAVP on Pascal 3D+ [21] ) and significantly
faster (46 frames per second (FPS) on a TITAN X GPU). This approach to
detection and rough pose estimation is fast and accurate enough to be widely
applied as a pre-processing step for tasks including high-accuracy pose
estimation, object tracking and localization, and vSLAM
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
Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd
Object detection and 6D pose estimation in the crowd (scenes with multiple
object instances, severe foreground occlusions and background distractors), has
become an important problem in many rapidly evolving technological areas such
as robotics and augmented reality. Single shot-based 6D pose estimators with
manually designed features are still unable to tackle the above challenges,
motivating the research towards unsupervised feature learning and
next-best-view estimation. In this work, we present a complete framework for
both single shot-based 6D object pose estimation and next-best-view prediction
based on Hough Forests, the state of the art object pose estimator that
performs classification and regression jointly. Rather than using manually
designed features we a) propose an unsupervised feature learnt from
depth-invariant patches using a Sparse Autoencoder and b) offer an extensive
evaluation of various state of the art features. Furthermore, taking advantage
of the clustering performed in the leaf nodes of Hough Forests, we learn to
estimate the reduction of uncertainty in other views, formulating the problem
of selecting the next-best-view. To further improve pose estimation, we propose
an improved joint registration and hypotheses verification module as a final
refinement step to reject false detections. We provide two additional
challenging datasets inspired from realistic scenarios to extensively evaluate
the state of the art and our framework. One is related to domestic environments
and the other depicts a bin-picking scenario mostly found in industrial
settings. We show that our framework significantly outperforms state of the art
both on public and on our datasets.Comment: CVPR 2016 accepted paper, project page:
http://www.iis.ee.ic.ac.uk/rkouskou/6D_NBV.htm
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