23,934 research outputs found
Object-based 2D-to-3D video conversion for effective stereoscopic content generation in 3D-TV applications
Three-dimensional television (3D-TV) has gained increasing popularity in the broadcasting domain, as it enables enhanced viewing experiences in comparison to conventional two-dimensional (2D) TV. However, its application has been constrained due to the lack of essential contents, i.e., stereoscopic videos. To alleviate such content shortage, an economical and practical solution is to reuse the huge media resources that are available in monoscopic 2D and convert them to stereoscopic 3D. Although stereoscopic video can be generated from monoscopic sequences using depth measurements extracted from cues like focus blur, motion and size, the quality of the resulting video may be poor as such measurements are usually arbitrarily defined and appear inconsistent with the real scenes. To help solve this problem, a novel method for object-based stereoscopic video generation is proposed which features i) optical-flow based occlusion reasoning in determining depth ordinal, ii) object segmentation using improved region-growing from masks of determined depth layers, and iii) a hybrid depth estimation scheme using content-based matching (inside a small library of true stereo image pairs) and depth-ordinal based regularization. Comprehensive experiments have validated the effectiveness of our proposed 2D-to-3D conversion method in generating stereoscopic videos of consistent depth measurements for 3D-TV applications
3D view of transient horizontal magnetic fields in the photosphere
We infer the 3D magnetic structure of a transient horizontal magnetic field
(THMF) during its evolution through the photosphere using SIRGAUS inversion
code. The SIRGAUS code is a modified version of SIR (Stokes Inversion based on
Response function), and allows for retrieval of information on the magnetic and
thermodynamic parameters of the flux tube embedded in the atmosphere from the
observed Stokes profiles. Spectro-polarimetric observations of the quiet Sun at
the disk center were performed with the Solar Optical Telescope (SOT) on board
Hinode with Fe I 630.2 nm lines. Using repetitive scans with a cadence of 130
s, we first detect the horizontal field that appears inside a granule, near its
edge. On the second scan, vertical fields with positive and negative polarities
appear at both ends of the horizontal field. Then, the horizontal field
disappears leaving the bipolar vertical magnetic fields. The results from the
inversion of the Stokes spectra clearly point to the existence of a flux tube
with magnetic field strength of G rising through the line forming
layer of the Fe I 630.2 nm lines. The flux tube is located at around
at =0 s and around
at =130 s. At =260 s the horizontal part is already above
the line forming region of the analyzed lines. The observed Doppler velocity is
maximally 3 km s, consistent with the upward motion of the structure as
retrieved from the SIRGAUS code. The vertical size of the tube is smaller than
the thickness of the line forming layer. The THMF has a clear
-shaped-loop structure with the apex located near the edge of a
granular cell. The magnetic flux carried by this THMF is estimated to be
Mx.Comment: 35 pages, 9 figures, Accepted for publication in Ap
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
We present a compact but effective CNN model for optical flow, called
PWC-Net. PWC-Net has been designed according to simple and well-established
principles: pyramidal processing, warping, and the use of a cost volume. Cast
in a learnable feature pyramid, PWC-Net uses the cur- rent optical flow
estimate to warp the CNN features of the second image. It then uses the warped
features and features of the first image to construct a cost volume, which is
processed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in
size and easier to train than the recent FlowNet2 model. Moreover, it
outperforms all published optical flow methods on the MPI Sintel final pass and
KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024x436)
images. Our models are available on https://github.com/NVlabs/PWC-Net.Comment: CVPR 2018 camera ready version (with github link to Caffe and PyTorch
code
Multiframe Scene Flow with Piecewise Rigid Motion
We introduce a novel multiframe scene flow approach that jointly optimizes
the consistency of the patch appearances and their local rigid motions from
RGB-D image sequences. In contrast to the competing methods, we take advantage
of an oversegmentation of the reference frame and robust optimization
techniques. We formulate scene flow recovery as a global non-linear least
squares problem which is iteratively solved by a damped Gauss-Newton approach.
As a result, we obtain a qualitatively new level of accuracy in RGB-D based
scene flow estimation which can potentially run in real-time. Our method can
handle challenging cases with rigid, piecewise rigid, articulated and moderate
non-rigid motion, and does not rely on prior knowledge about the types of
motions and deformations. Extensive experiments on synthetic and real data show
that our method outperforms state-of-the-art.Comment: International Conference on 3D Vision (3DV), Qingdao, China, October
201
Multiframe Scene Flow with Piecewise Rigid Motion
We introduce a novel multiframe scene flow approach that jointly optimizes
the consistency of the patch appearances and their local rigid motions from
RGB-D image sequences. In contrast to the competing methods, we take advantage
of an oversegmentation of the reference frame and robust optimization
techniques. We formulate scene flow recovery as a global non-linear least
squares problem which is iteratively solved by a damped Gauss-Newton approach.
As a result, we obtain a qualitatively new level of accuracy in RGB-D based
scene flow estimation which can potentially run in real-time. Our method can
handle challenging cases with rigid, piecewise rigid, articulated and moderate
non-rigid motion, and does not rely on prior knowledge about the types of
motions and deformations. Extensive experiments on synthetic and real data show
that our method outperforms state-of-the-art.Comment: International Conference on 3D Vision (3DV), Qingdao, China, October
201
Dense Motion Estimation for Smoke
Motion estimation for highly dynamic phenomena such as smoke is an open
challenge for Computer Vision. Traditional dense motion estimation algorithms
have difficulties with non-rigid and large motions, both of which are
frequently observed in smoke motion. We propose an algorithm for dense motion
estimation of smoke. Our algorithm is robust, fast, and has better performance
over different types of smoke compared to other dense motion estimation
algorithms, including state of the art and neural network approaches. The key
to our contribution is to use skeletal flow, without explicit point matching,
to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this
paper we describe our algorithm in greater detail, and provide experimental
evidence to support our claims.Comment: ACCV201
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