2,018 research outputs found
Two View Line-Based Motion and Structure Estimation for Planar Scenes
We present an algorithm for reconstruction of piece-wise planar scenes from only two views and based on minimum line correspondences. We first recover camera rotation by matching vanishing points based on the methods already exist in the literature and then recover the camera translation by searching among a family of hypothesized planes passing through one line. Unlike algorithms based on line segments, the presented algorithm does not require an overlap between two line segments or more that one line correspondence across more than two views to recover the translation and achieves the goal by exploiting photometric constraints of the surface around the line. Experimental results on real images prove the functionality of the algorithm
Exploiting Structural Regularities and Beyond: Vision-based Localization and Mapping in Man-Made Environments
Image-based estimation of camera motion, known as visual odometry
(VO), plays a very important role in many robotic applications
such as control and navigation of unmanned mobile robots,
especially when no external navigation reference signal is
available. The core problem of VO is the estimation of the
camera’s ego-motion (i.e. tracking) either between successive
frames, namely relative pose estimation, or with respect to a
global map, namely absolute pose estimation. This thesis aims to
develop efficient, accurate and robust VO solutions by taking
advantage of structural regularities in man-made environments,
such as piece-wise planar structures, Manhattan World and more
generally, contours and edges. Furthermore, to handle challenging
scenarios that are beyond the limits of classical sensor based VO
solutions, we investigate a recently emerging sensor — the
event camera and study on event-based mapping — one of the key
problems in the event-based VO/SLAM. The main achievements are
summarized as follows.
First, we revisit an old topic on relative pose estimation:
accurately and robustly estimating the fundamental matrix given a
collection of independently estimated homograhies. Three
classical methods are reviewed and then we show a simple but
nontrivial two-step normalization
within the direct linear method that achieves similar performance
to the less attractive and more computationally intensive
hallucinated points based method.
Second, an efficient 3D rotation estimation algorithm for depth
cameras in piece-wise planar environments is presented. It shows
that by using surface normal vectors as an input, planar modes in
the corresponding density distribution function can be discovered
and continuously
tracked using efficient non-parametric estimation techniques. The
relative rotation can be estimated by registering entire bundles
of planar modes by using robust L1-norm minimization.
Third, an efficient alternative to the iterative closest point
algorithm for real-time tracking of modern depth cameras in
ManhattanWorlds is developed. We exploit the common orthogonal
structure of man-made environments in order to decouple the
estimation of the rotation and the three degrees of freedom of
the translation. The derived camera orientation is absolute and
thus free of long-term drift, which in turn benefits the accuracy
of the translation estimation as well.
Fourth, we look into a more general structural
regularity—edges. A real-time VO system that uses Canny edges
is proposed for RGB-D cameras. Two novel alternatives to
classical distance transforms are developed with great properties
that significantly improve the classical Euclidean distance field
based methods in terms of efficiency, accuracy and robustness.
Finally, to deal with challenging scenarios that go beyond what
standard RGB/RGB-D cameras can handle, we investigate the
recently emerging event camera and focus on the problem of 3D
reconstruction from data captured by a stereo event-camera rig
moving in a static
scene, such as in the context of stereo Simultaneous Localization
and Mapping
Shape basis interpretation for monocular deformable 3D reconstruction
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we propose a novel interpretable shape model to encode object non-rigidity. We first use the initial frames of a monocular video to recover a rest shape, used later to compute a dissimilarity measure based on a distance matrix measurement. Spectral analysis is then applied to this matrix to obtain a reduced shape basis, that in contrast to existing approaches, can be physically interpreted. In turn, these pre-computed shape bases are used to linearly span the deformation of a wide variety of objects. We introduce the low-rank basis into a sequential approach to recover both camera motion and non-rigid shape from the monocular video, by simply optimizing the weights of the linear combination using bundle adjustment. Since the number of parameters to optimize per frame is relatively small, specially when physical priors are considered, our approach is fast and can potentially run in real time. Validation is done in a wide variety of real-world objects, undergoing both inextensible and extensible deformations. Our approach achieves remarkable robustness to artifacts such as noisy and missing measurements and shows an improved performance to competing methods.Peer ReviewedPostprint (author's final draft
Joint Optical Flow and Temporally Consistent Semantic Segmentation
The importance and demands of visual scene understanding have been steadily
increasing along with the active development of autonomous systems.
Consequently, there has been a large amount of research dedicated to semantic
segmentation and dense motion estimation. In this paper, we propose a method
for jointly estimating optical flow and temporally consistent semantic
segmentation, which closely connects these two problem domains and leverages
each other. Semantic segmentation provides information on plausible physical
motion to its associated pixels, and accurate pixel-level temporal
correspondences enhance the accuracy of semantic segmentation in the temporal
domain. We demonstrate the benefits of our approach on the KITTI benchmark,
where we observe performance gains for flow and segmentation. We achieve
state-of-the-art optical flow results, and outperform all published algorithms
by a large margin on challenging, but crucial dynamic objects.Comment: 14 pages, Accepted for CVRSUAD workshop at ECCV 201
Low-level Vision by Consensus in a Spatial Hierarchy of Regions
We introduce a multi-scale framework for low-level vision, where the goal is
estimating physical scene values from image data---such as depth from stereo
image pairs. The framework uses a dense, overlapping set of image regions at
multiple scales and a "local model," such as a slanted-plane model for stereo
disparity, that is expected to be valid piecewise across the visual field.
Estimation is cast as optimization over a dichotomous mixture of variables,
simultaneously determining which regions are inliers with respect to the local
model (binary variables) and the correct co-ordinates in the local model space
for each inlying region (continuous variables). When the regions are organized
into a multi-scale hierarchy, optimization can occur in an efficient and
parallel architecture, where distributed computational units iteratively
perform calculations and share information through sparse connections between
parents and children. The framework performs well on a standard benchmark for
binocular stereo, and it produces a distributional scene representation that is
appropriate for combining with higher-level reasoning and other low-level cues.Comment: Accepted to CVPR 2015. Project page:
http://www.ttic.edu/chakrabarti/consensus
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