3,454 research outputs found
Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data
Reliable human pose estimation (HPE) is essential to many clinical
applications, such as surgical workflow analysis, radiation safety monitoring
and human-robot cooperation. Proposed methods for the operating room (OR) rely
either on foreground estimation using a multi-camera system, which is a
challenge in real ORs due to color similarities and frequent illumination
changes, or on wearable sensors or markers, which are invasive and therefore
difficult to introduce in the room. Instead, we propose a novel approach based
on Pictorial Structures (PS) and on RGB-D data, which can be easily deployed in
real ORs. We extend the PS framework in two ways. First, we build robust and
discriminative part detectors using both color and depth images. We also
present a novel descriptor for depth images, called histogram of depth
differences (HDD). Second, we extend PS to 3D by proposing 3D pairwise
constraints and a new method that makes exact inference tractable. Our approach
is evaluated for pose estimation and clinician detection on a challenging RGB-D
dataset recorded in a busy operating room during live surgeries. We conduct
series of experiments to study the different part detectors in conjunction with
the various 2D or 3D pairwise constraints. Our comparisons demonstrate that 3D
PS with RGB-D part detectors significantly improves the results in a visually
challenging operating environment.Comment: The supplementary video is available at https://youtu.be/iabbGSqRSg
Growing Regression Forests by Classification: Applications to Object Pose Estimation
In this work, we propose a novel node splitting method for regression trees
and incorporate it into the regression forest framework. Unlike traditional
binary splitting, where the splitting rule is selected from a predefined set of
binary splitting rules via trial-and-error, the proposed node splitting method
first finds clusters of the training data which at least locally minimize the
empirical loss without considering the input space. Then splitting rules which
preserve the found clusters as much as possible are determined by casting the
problem into a classification problem. Consequently, our new node splitting
method enjoys more freedom in choosing the splitting rules, resulting in more
efficient tree structures. In addition to the Euclidean target space, we
present a variant which can naturally deal with a circular target space by the
proper use of circular statistics. We apply the regression forest employing our
node splitting to head pose estimation (Euclidean target space) and car
direction estimation (circular target space) and demonstrate that the proposed
method significantly outperforms state-of-the-art methods (38.5% and 22.5%
error reduction respectively).Comment: Paper accepted by ECCV 201
Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres
Many computer vision challenges require continuous outputs, but tend to be
solved by discrete classification. The reason is classification's natural
containment within a probability -simplex, as defined by the popular softmax
activation function. Regular regression lacks such a closed geometry, leading
to unstable training and convergence to suboptimal local minima. Starting from
this insight we revisit regression in convolutional neural networks. We observe
many continuous output problems in computer vision are naturally contained in
closed geometrical manifolds, like the Euler angles in viewpoint estimation or
the normals in surface normal estimation. A natural framework for posing such
continuous output problems are -spheres, which are naturally closed
geometric manifolds defined in the space. By introducing a
spherical exponential mapping on -spheres at the regression output, we
obtain well-behaved gradients, leading to stable training. We show how our
spherical regression can be utilized for several computer vision challenges,
specifically viewpoint estimation, surface normal estimation and 3D rotation
estimation. For all these problems our experiments demonstrate the benefit of
spherical regression. All paper resources are available at
https://github.com/leoshine/Spherical_Regression.Comment: CVPR 2019 camera read
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Many robotics applications require precise pose estimates despite operating
in large and changing environments. This can be addressed by visual
localization, using a pre-computed 3D model of the surroundings. The pose
estimation then amounts to finding correspondences between 2D keypoints in a
query image and 3D points in the model using local descriptors. However,
computational power is often limited on robotic platforms, making this task
challenging in large-scale environments. Binary feature descriptors
significantly speed up this 2D-3D matching, and have become popular in the
robotics community, but also strongly impair the robustness to perceptual
aliasing and changes in viewpoint, illumination and scene structure. In this
work, we propose to leverage recent advances in deep learning to perform an
efficient hierarchical localization. We first localize at the map level using
learned image-wide global descriptors, and subsequently estimate a precise pose
from 2D-3D matches computed in the candidate places only. This restricts the
local search and thus allows to efficiently exploit powerful non-binary
descriptors usually dismissed on resource-constrained devices. Our approach
results in state-of-the-art localization performance while running in real-time
on a popular mobile platform, enabling new prospects for robotics research.Comment: CoRL 2018 Camera-ready (fix typos and update citations
End-to-end Recovery of Human Shape and Pose
We describe Human Mesh Recovery (HMR), an end-to-end framework for
reconstructing a full 3D mesh of a human body from a single RGB image. In
contrast to most current methods that compute 2D or 3D joint locations, we
produce a richer and more useful mesh representation that is parameterized by
shape and 3D joint angles. The main objective is to minimize the reprojection
loss of keypoints, which allow our model to be trained using images in-the-wild
that only have ground truth 2D annotations. However, the reprojection loss
alone leaves the model highly under constrained. In this work we address this
problem by introducing an adversary trained to tell whether a human body
parameter is real or not using a large database of 3D human meshes. We show
that HMR can be trained with and without using any paired 2D-to-3D supervision.
We do not rely on intermediate 2D keypoint detections and infer 3D pose and
shape parameters directly from image pixels. Our model runs in real-time given
a bounding box containing the person. We demonstrate our approach on various
images in-the-wild and out-perform previous optimization based methods that
output 3D meshes and show competitive results on tasks such as 3D joint
location estimation and part segmentation.Comment: CVPR 2018, Project page with code: https://akanazawa.github.io/hmr
Pose Induction for Novel Object Categories
We address the task of predicting pose for objects of unannotated object
categories from a small seed set of annotated object classes. We present a
generalized classifier that can reliably induce pose given a single instance of
a novel category. In case of availability of a large collection of novel
instances, our approach then jointly reasons over all instances to improve the
initial estimates. We empirically validate the various components of our
algorithm and quantitatively show that our method produces reliable pose
estimates. We also show qualitative results on a diverse set of classes and
further demonstrate the applicability of our system for learning shape models
of novel object classes
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