20,273 research outputs found
On the Issue of Camera Calibration with Narrow Angular Field of View
This paper considers the issue of calibrating a
camera with narrow angular field of view using standard, perspective
methods in computer vision. In doing so, the significance
of perspective distortion both for camera calibration and for
pose estimation is revealed. Since narrow angular field of view
cameras make it difficult to obtain rich images in terms of perspectivity,
the accuracy of the calibration results is expectedly low.
From this, we propose an alternative method that compensates for
this loss by utilizing the pose readings of a robotic manipulator.
It facilitates accurate pose estimation by nonlinear optimization,
minimizing reprojection errors and errors in the manipulator
transformations at the same time. Accurate pose estimation in
turn enables accurate parametrization of a perspective camera
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
In this paper we address the problem of multiple camera calibration in the
presence of a homogeneous scene, and without the possibility of employing
calibration object based methods. The proposed solution exploits salient
features present in a larger field of view, but instead of employing active
vision we replace the cameras with stereo rigs featuring a long focal analysis
camera, as well as a short focal registration camera. Thus, we are able to
propose an accurate solution which does not require intrinsic variation models
as in the case of zooming cameras. Moreover, the availability of the two views
simultaneously in each rig allows for pose re-estimation between rigs as often
as necessary. The algorithm has been successfully validated in an indoor
setting, as well as on a difficult scene featuring a highly dense pilgrim crowd
in Makkah.Comment: 13 pages, 6 figures, submitted to Machine Vision and Application
Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty
It is well known that the accuracy of a calibration depends strongly on the
choice of camera poses from which images of a calibration object are acquired.
We present a system -- Calibration Wizard -- that interactively guides a user
towards taking optimal calibration images. For each new image to be taken, the
system computes, from all previously acquired images, the pose that leads to
the globally maximum reduction of expected uncertainty on intrinsic parameters
and then guides the user towards that pose. We also show how to incorporate
uncertainty in corner point position in a novel principled manner, for both,
calibration and computation of the next best pose. Synthetic and real-world
experiments are performed to demonstrate the effectiveness of Calibration
Wizard.Comment: Oral presentation at ICCV 201
Tex2Shape: Detailed Full Human Body Geometry From a Single Image
We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method
Tex2Shape: Detailed Full Human Body Geometry From a Single Image
We present a simple yet effective method to infer detailed full human body
shape from only a single photograph. Our model can infer full-body shape
including face, hair, and clothing including wrinkles at interactive
frame-rates. Results feature details even on parts that are occluded in the
input image. Our main idea is to turn shape regression into an aligned
image-to-image translation problem. The input to our method is a partial
texture map of the visible region obtained from off-the-shelf methods. From a
partial texture, we estimate detailed normal and vector displacement maps,
which can be applied to a low-resolution smooth body model to add detail and
clothing. Despite being trained purely with synthetic data, our model
generalizes well to real-world photographs. Numerous results demonstrate the
versatility and robustness of our method
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