1,906 research outputs found
Entropy-difference based stereo error detection
Stereo depth estimation is error-prone; hence, effective error detection
methods are desirable. Most such existing methods depend on characteristics of
the stereo matching cost curve, making them unduly dependent on functional
details of the matching algorithm. As a remedy, we propose a novel error
detection approach based solely on the input image and its depth map. Our
assumption is that, entropy of any point on an image will be significantly
higher than the entropy of its corresponding point on the image's depth map. In
this paper, we propose a confidence measure, Entropy-Difference (ED) for stereo
depth estimates and a binary classification method to identify incorrect
depths. Experiments on the Middlebury dataset show the effectiveness of our
method. Our proposed stereo confidence measure outperforms 17 existing measures
in all aspects except occlusion detection. Established metrics such as
precision, accuracy, recall, and area-under-curve are used to demonstrate the
effectiveness of our method
Occlusion Aware Unsupervised Learning of Optical Flow
It has been recently shown that a convolutional neural network can learn
optical flow estimation with unsupervised learning. However, the performance of
the unsupervised methods still has a relatively large gap compared to its
supervised counterpart. Occlusion and large motion are some of the major
factors that limit the current unsupervised learning of optical flow methods.
In this work we introduce a new method which models occlusion explicitly and a
new warping way that facilitates the learning of large motion. Our method shows
promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets.
Especially on KITTI dataset where abundant unlabeled samples exist, our
unsupervised method outperforms its counterpart trained with supervised
learning.Comment: CVPR 2018 Camera-read
Cell-based approach for 3D reconstruction from incomplete silhouettes
Shape-from-silhouettes is a widely adopted approach to compute accurate 3D reconstructions of people or objects in a multi-camera environment. However, such algorithms are traditionally very sensitive to errors in the silhouettes due to imperfect foreground-background estimation or occluding objects appearing in front of the object of interest. We propose a novel algorithm that is able to still provide high quality reconstruction from incomplete silhouettes. At the core of the method is the partitioning of reconstruction space in cells, i.e. regions with uniform camera and silhouette coverage properties. A set of rules is proposed to iteratively add cells to the reconstruction based on their potential to explain discrepancies between silhouettes in different cameras. Experimental analysis shows significantly improved F1-scores over standard leave-M-out reconstruction techniques
MRF Stereo Matching with Statistical Estimation of Parameters
For about the last ten years, stereo matching in computer vision has been treated as a combinatorial optimization problem. Assuming that the points in stereo images form a Markov Random Field (MRF), a variety of combinatorial optimization algorithms has been developed to optimize their underlying cost functions. In many of these algorithms, the MRF parameters of the cost functions have often been manually tuned or heuristically determined for achieving good performance results. Recently, several algorithms for statistical, hence, automatic estimation of the parameters have been published. Overall, these algorithms perform well in labeling, but they lack in performance for handling discontinuity in labeling along the surface borders.
In this dissertation, we develop an algorithm for optimization of the cost function with automatic estimation of the MRF parameters – the data and smoothness parameters. Both the parameters are estimated statistically and applied in the cost function with support of adaptive neighborhood defined based on color similarity. With the proposed algorithm, discontinuity handling with higher consistency than of the existing algorithms is achieved along surface borders. The data parameters are pre-estimated from one of the stereo images by applying a hypothesis, called noise equivalence hypothesis, to eliminate interdependency between the estimations of the data and smoothness parameters. The smoothness parameters are estimated applying a combination of maximum likelihood and disparity gradient constraint, to eliminate nested inference for the estimation. The parameters for handling discontinuities in data and smoothness are defined statistically as well. We model cost functions to match the images symmetrically for improved matching performance and also to detect occlusions. Finally, we fill the occlusions in the disparity map by applying several existing and proposed algorithms and show that our best proposed segmentation based least squares algorithm performs better than the existing algorithms.
We conduct experiments with the proposed algorithm on publicly available ground truth test datasets provided by the Middlebury College. Experiments show that results better than the existing algorithms’ are delivered by the proposed algorithm having the MRF parameters estimated automatically. In addition, applying the parameter estimation technique in existing stereo matching algorithm, we observe significant improvement in computational time
Cross-Scale Cost Aggregation for Stereo Matching
Human beings process stereoscopic correspondence across multiple scales.
However, this bio-inspiration is ignored by state-of-the-art cost aggregation
methods for dense stereo correspondence. In this paper, a generic cross-scale
cost aggregation framework is proposed to allow multi-scale interaction in cost
aggregation. We firstly reformulate cost aggregation from a unified
optimization perspective and show that different cost aggregation methods
essentially differ in the choices of similarity kernels. Then, an inter-scale
regularizer is introduced into optimization and solving this new optimization
problem leads to the proposed framework. Since the regularization term is
independent of the similarity kernel, various cost aggregation methods can be
integrated into the proposed general framework. We show that the cross-scale
framework is important as it effectively and efficiently expands
state-of-the-art cost aggregation methods and leads to significant
improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.Comment: To Appear in 2013 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). 2014 (poster, 29.88%
Disparity-compensated view synthesis for s3D content correction
International audienceThe production of stereoscopic 3D HD content is considerably increasing and experience in 2-view acquisition is in progress. High quality material to the audience is required but not always ensured, and correction of the stereo views may be required. This is done via disparity-compensated view synthesis. A robust method has been developed dealing with these acquisition problems that introduce discomfort (e.g hyperdivergence and hyperconvergence...) as well as those ones that may disrupt the correction itself (vertical disparity, color difference between views...). The method has three phases: a preprocessing in order to correct the stereo images and estimate features (e.g. disparity range...) over the sequence. The second (main) phase proceeds then to disparity estimation and view synthesis. Dual disparity estimation based on robust block-matching, discontinuity-preserving filtering, consistency and occlusion handling has been developed. Accurate view synthesis is carried out through disparity compensation. Disparity assessment has been introduced in order to detect and quantify errors. A post-processing deals with these errors as a fallback mode. The paper focuses on disparity estimation and view synthesis of HD images. Quality assessment of synthesized views on a large set of HD video data has proved the effectiveness of our method
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