4,851 research outputs found
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
Towards Automatic SAR-Optical Stereogrammetry over Urban Areas using Very High Resolution Imagery
In this paper we discuss the potential and challenges regarding SAR-optical
stereogrammetry for urban areas, using very-high-resolution (VHR) remote
sensing imagery. Since we do this mainly from a geometrical point of view, we
first analyze the height reconstruction accuracy to be expected for different
stereogrammetric configurations. Then, we propose a strategy for simultaneous
tie point matching and 3D reconstruction, which exploits an epipolar-like
search window constraint. To drive the matching and ensure some robustness, we
combine different established handcrafted similarity measures. For the
experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and
MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR
imagery is generally feasible with 3D positioning accuracies in the
meter-domain, although the matching of these strongly hetereogeneous
multi-sensor data remains very challenging. Keywords: Synthetic Aperture Radar
(SAR), optical images, remote sensing, data fusion, stereogrammetr
Trifocal Relative Pose from Lines at Points and its Efficient Solution
We present a new minimal problem for relative pose estimation mixing point
features with lines incident at points observed in three views and its
efficient homotopy continuation solver. We demonstrate the generality of the
approach by analyzing and solving an additional problem with mixed point and
line correspondences in three views. The minimal problems include
correspondences of (i) three points and one line and (ii) three points and two
lines through two of the points which is reported and analyzed here for the
first time. These are difficult to solve, as they have 216 and - as shown here
- 312 solutions, but cover important practical situations when line and point
features appear together, e.g., in urban scenes or when observing curves. We
demonstrate that even such difficult problems can be solved robustly using a
suitable homotopy continuation technique and we provide an implementation
optimized for minimal problems that can be integrated into engineering
applications. Our simulated and real experiments demonstrate our solvers in the
camera geometry computation task in structure from motion. We show that new
solvers allow for reconstructing challenging scenes where the standard two-view
initialization of structure from motion fails.Comment: This material is based upon work supported by the National Science
Foundation under Grant No. DMS-1439786 while most authors were in residence
at Brown University's Institute for Computational and Experimental Research
in Mathematics -- ICERM, in Providence, R
Learning to Find Good Correspondences
We develop a deep architecture to learn to find good correspondences for
wide-baseline stereo. Given a set of putative sparse matches and the camera
intrinsics, we train our network in an end-to-end fashion to label the
correspondences as inliers or outliers, while simultaneously using them to
recover the relative pose, as encoded by the essential matrix. Our architecture
is based on a multi-layer perceptron operating on pixel coordinates rather than
directly on the image, and is thus simple and small. We introduce a novel
normalization technique, called Context Normalization, which allows us to
process each data point separately while imbuing it with global information,
and also makes the network invariant to the order of the correspondences. Our
experiments on multiple challenging datasets demonstrate that our method is
able to drastically improve the state of the art with little training data.Comment: CVPR 2018 (Oral
ProSLAM: Graph SLAM from a Programmer's Perspective
In this paper we present ProSLAM, a lightweight stereo visual SLAM system
designed with simplicity in mind. Our work stems from the experience gathered
by the authors while teaching SLAM to students and aims at providing a highly
modular system that can be easily implemented and understood. Rather than
focusing on the well known mathematical aspects of Stereo Visual SLAM, in this
work we highlight the data structures and the algorithmic aspects that one
needs to tackle during the design of such a system. We implemented ProSLAM
using the C++ programming language in combination with a minimal set of well
known used external libraries. In addition to an open source implementation, we
provide several code snippets that address the core aspects of our approach
directly in this paper. The results of a thorough validation performed on
standard benchmark datasets show that our approach achieves accuracy comparable
to state of the art methods, while requiring substantially less computational
resources.Comment: 8 pages, 8 figure
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