1,203 research outputs found
Robust Camera Location Estimation by Convex Programming
D structure recovery from a collection of D images requires the
estimation of the camera locations and orientations, i.e. the camera motion.
For large, irregular collections of images, existing methods for the location
estimation part, which can be formulated as the inverse problem of estimating
locations in
from noisy measurements of a subset of the pairwise directions
, are
sensitive to outliers in direction measurements. In this paper, we firstly
provide a complete characterization of well-posed instances of the location
estimation problem, by presenting its relation to the existing theory of
parallel rigidity. For robust estimation of camera locations, we introduce a
two-step approach, comprised of a pairwise direction estimation method robust
to outliers in point correspondences between image pairs, and a convex program
to maintain robustness to outlier directions. In the presence of partially
corrupted measurements, we empirically demonstrate that our convex formulation
can even recover the locations exactly. Lastly, we demonstrate the utility of
our formulations through experiments on Internet photo collections.Comment: 10 pages, 6 figures, 3 table
Distributed human 3D pose estimation and action recognition.
In this paper, we propose a distributed solution for3D human pose estimation using a RGBD camera network. Thekey feature of our method is a dynamic hybrid consensus filter(DHCF) is introduced to fuse the multiple view informationof cameras. In contrast to the centralized fusion solution,the DHCF algorithm can be used in a distributed network,which requires no central information fusion center. Therefore,the DHCF based fusion algorithm can benefit from manyadvantages of distributed network. We also show that theproposed fusion algorithm can handle the occlusion problemseffectively, and achieve higher action recognition rate comparedto the ones using only single view information
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
In this work we address the problem of finding reliable pixel-level
correspondences under difficult imaging conditions. We propose an approach
where a single convolutional neural network plays a dual role: It is
simultaneously a dense feature descriptor and a feature detector. By postponing
the detection to a later stage, the obtained keypoints are more stable than
their traditional counterparts based on early detection of low-level
structures. We show that this model can be trained using pixel correspondences
extracted from readily available large-scale SfM reconstructions, without any
further annotations. The proposed method obtains state-of-the-art performance
on both the difficult Aachen Day-Night localization dataset and the InLoc
indoor localization benchmark, as well as competitive performance on other
benchmarks for image matching and 3D reconstruction.Comment: Accepted at CVPR 201
Efficient Many-Light Rendering of Scenes with Participating Media
We present several approaches based on virtual lights that aim at capturing the light transport without compromising quality, and while preserving the elegance and efficiency of many-light rendering. By reformulating the integration scheme, we obtain two numerically efficient techniques; one tailored specifically for interactive, high-quality lighting on surfaces, and one for handling scenes with participating media
Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition
This paper deals with the rotation synchronization problem, which arises in
global registration of 3D point-sets and in structure from motion. The problem
is formulated in an unprecedented way as a "low-rank and sparse" matrix
decomposition that handles both outliers and missing data. A minimization
strategy, dubbed R-GoDec, is also proposed and evaluated experimentally against
state-of-the-art algorithms on simulated and real data. The results show that
R-GoDec is the fastest among the robust algorithms.Comment: The material contained in this paper is part of a manuscript
submitted to CVI
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