scholarly journals Fast global stereo matching via energy pyramid minimization

2014 ◽  
Vol II-3 ◽  
pp. 41-48
Author(s):  
B. Conejo ◽  
S. Leprince ◽  
F. Ayoub ◽  
J. P. Avouac
Keyword(s):  
Random Fields ◽  
Stereo Matching ◽  
Conditional Random Fields ◽  
Optimal Solution ◽  
Stereo Pair ◽  
Optimization Approach ◽  
Disparity Map ◽  
Image Pyramids ◽  
Global Matching ◽  
Coarse To Fine

We define a global matching framework based on energy pyramid, the Global Matching via Energy Pyramid (GM-EP) algorithm, which estimates the disparity map from a single stereo-pair by solving an energy minimization problem. We efficiently address this minimization by globally optimizing a coarse to fine sequence of sparse Conditional Random Fields (CRF) directly defined on the energy. This global discrete optimization approach guarantees that at each scale we obtain a near optimal solution, and we demonstrate its superiority over state of the art image pyramid approaches through application to real stereo-pairs. We conclude that multiscale approaches should be build on energy pyramids rather than on image pyramids.

scholarly journals THE EFFECT OF SHADOW AREA ON SGM ALGORITHM AND DISPARITY MAP REFINEMENT FROM HIGH RESOLUTION SATELLITE STEREO IMAGES

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 281-286
Author(s):  
N. Tatar ◽  
M. Saadatseresht ◽  
H. Arefi
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
High Performance ◽  
Stereo Matching ◽  
Image Data ◽  
Stereo Pair ◽  
Stereo Images ◽  
Disparity Map ◽  
Shadow Area ◽  
Global Matching

Semi Global Matching (SGM) algorithm is known as a high performance and reliable stereo matching algorithm in photogrammetry community. However, there are some challenges using this algorithm especially for high resolution satellite stereo images over urban areas and images with shadow areas. As it can be seen, unfortunately the SGM algorithm computes highly noisy disparity values for shadow areas around the tall neighborhood buildings due to mismatching in these lower entropy areas. In this paper, a new method is developed to refine the disparity map in shadow areas. The method is based on the integration of potential of panchromatic and multispectral image data to detect shadow areas in object level. In addition, a RANSAC plane fitting and morphological filtering are employed to refine the disparity map. The results on a stereo pair of GeoEye-1 captured over Qom city in Iran, shows a significant increase in the rate of matched pixels compared to standard SGM algorithm.

scholarly journals A Joint 2D-3D Complementary Network for Stereo Matching

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1430
Author(s):  
Xiaogang Jia ◽  
Wei Chen ◽  
Zhengfa Liang ◽  
Xin Luo ◽  
Mingfei Wu ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Computational Cost ◽  
Research Field ◽  
Disparity Map ◽  
Improve Performance ◽  
Cost Aggregation ◽  
Disparity Range ◽  
Public Datasets ◽  
Coarse To Fine ◽  
Speed And Accuracy

Stereo matching is an important research field of computer vision. Due to the dimension of cost aggregation, current neural network-based stereo methods are difficult to trade-off speed and accuracy. To this end, we integrate fast 2D stereo methods with accurate 3D networks to improve performance and reduce running time. We leverage a 2D encoder-decoder network to generate a rough disparity map and construct a disparity range to guide the 3D aggregation network, which can significantly improve the accuracy and reduce the computational cost. We use a stacked hourglass structure to refine the disparity from coarse to fine. We evaluated our method on three public datasets. According to the KITTI official website results, Our network can generate an accurate result in 80 ms on a modern GPU. Compared to other 2D stereo networks (AANet, DeepPruner, FADNet, etc.), our network has a big improvement in accuracy. Meanwhile, it is significantly faster than other 3D stereo networks (5× than PSMNet, 7.5× than CSN and 22.5× than GANet, etc.), demonstrating the effectiveness of our method.

scholarly journals Disparity estimation using Graph cuts for road applications

2021 ◽  
Vol 297 ◽  
pp. 01055
Author(s):  
Mohamed El Ansari ◽  
Ilyas El Jaafari ◽  
Lahcen Koutti
Keyword(s):  
Stereo Matching ◽  
Optimal Solution ◽  
Similarity Criterion ◽  
Graph Cuts ◽  
Search Space ◽  
Disparity Estimation ◽  
Stereo Images ◽  
Disparity Map ◽  
Current Frame ◽  
Edge Points

This paper proposes a new edge based stereo matching approach for road applications. The new approach consists in matching the edge points extracted from the input stereo images using temporal constraints. At the current frame, we propose to estimate a disparity range for each image line based on the disparity map of its preceding one. The stereo images are divided into multiple parts according to the estimated disparity ranges. The optimal solution of each part is independently approximated via the state-of-the-art energy minimization approach Graph cuts. The disparity search space at each image part is very small compared to the global one, which improves the results and reduces the execution time. Furthermore, as a similarity criterion between corresponding edge points, we propose a new cost function based on the intensity, the gradient magnitude and gradient orientation. The proposed method has been tested on virtual stereo images, and it has been compared to a recently proposed method and the results are satisfactory.

A Parallel Stereo Matching Algorithm Core for FPGA Modeled by DSP Builder

2014 ◽  
Vol 536-537 ◽  
pp. 67-76
Author(s):  
Xiang Zhang ◽  
Zhang Wei Chen
Keyword(s):  
Stereo Matching ◽  
Cost Calculation ◽  
Stereo Pair ◽  
Disparity Map ◽  
High Definition ◽  
Matching Algorithm ◽  
Census Transform ◽  
Cost Aggregation ◽  
Dsp Builder ◽  
Fpga Chip

This paper proposes a FPGA implementation to apply a stereo matching algorithm based on a kind of sparse census transform in a FPGA chip which can provide a high-definition dense disparity map in real-time. The parallel stereo matching algorithm core involves census transform, cost calculation and cost aggregation modules. The circuits of the algorithm core are modeled by the Matlab/Simulink-based tool box: DSP Builder. The system can process many different sizes of stereo pair images through a configuration interface. The maximum horizon resolution of stereo images is 2048.

scholarly journals 3D Stereo Reconstruction of SEM Images

2018 ◽  
Vol 12 (12) ◽  
pp. 57
Author(s):  
J. C. Henao-Londoño ◽  
J. C. Riaño-Rojas ◽  
J. B. Gómez-Mendoza ◽  
E. Restrepo-Parra
Keyword(s):  
Dynamic Programming ◽  
Stereo Matching ◽  
Materials Science ◽  
Parameters Estimation ◽  
Pixel Size ◽  
Disparity Map ◽  
Sem Images ◽  
Stereo Reconstruction ◽  
Global Matching ◽  
Reconstructed Surface

In this work is proposed a new fully automated methodology using computer vision and dynamic programming to obtain a 3D reconstruction model of surfaces using scanning electron microscope (SEM) images based on stereovision. The horizontal stereo matching step is done with a robust and efficient algorithm based on semi-global matching. The cost function used in this study is very simple since the brightness and contrast change of corresponding pixels is negligible for the small tilt involved in stereo SEM. It is used a sum of absolute differences (SAD) over a variable pixel size window. Since it relies on dynamic programming, the matching algorithm uses an occlusion parameter which penalizes large depth discontinuities and, in practice, smooths the disparity map and the corresponding reconstructed surface. This step yields a disparity map, i.e. the differences between the horizontal coordinates of the matching points in the stereo images. The horizontal disparity map is finally converted into heights according to the SEM acquisition parameters: tilt angle, image magnification and pixel size. A validation test was first performed using as reference a microscopic grid with manufacturer specifications. Finally, with the 3D model are proposed some applications in materials science as roughness parameters estimation and wear measurements.

3D-Modeling Depth Optimization Using a Neural Network and Image Segments

2013 ◽  
Vol 333-335 ◽  
pp. 1096-1105 ◽  
Author(s):  
Fan Jun Liu ◽  
Bin Gang Cao
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Fourier Transformation ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Reference Image ◽  
Fast Fourier Transformation ◽  
Stereo Pair ◽  
Disparity Map ◽  
Ground Truth Data

We present a 3D(three-dimensional)-modeling disparity-map optimization algorithm using a neural network and image segments for stereo navigation. We decompose the optimization algorithm problem into two sub-problems: initial stereo matching and depth optimization. A two-step procedure is proposed to solve the sub-problems sequentially. The first step is a region based NCC(normalized cross-correlation) matching process. But we use fast Fourier transformation and inverse fast Fourier transformation to eliminate redundant calculations in NCC, and we create a high-confidence disparity map by cross checking. In the second step, the reference image (the left image of the inputted stereo pair) is segmented into regions according to homogeneous color. A neural network is then built to model the three dimensional surface and applied to refine disparities in each image segment. The experimental results obtained for Middlebury test datasets and real stereo road images indicate that our method is competitive with the best stereo matching algorithms currently available. In particular, the approach has significantly improved performance for road images used in navigation and the disparity maps recovered by our algorithm are similar to ground truth data.

scholarly journals HYBRID-BASED DENSE STEREO MATCHING

2016 ◽  
Vol XLI-B3 ◽  
pp. 495-501 ◽  
Author(s):  
T. Y. Chuang ◽  
H. W. Ting ◽  
J. J. Jaw
Keyword(s):  
Stereo Matching ◽  
Penalty Parameter ◽  
Disparity Map ◽  
Cost Aggregation ◽  
Satisfactory Performance ◽  
Dense Stereo Matching ◽  
Illumination Changes ◽  
Global Matching ◽  
Disparity Maps ◽  
Image Edges

Stereo matching generating accurate and dense disparity maps is an indispensable technique for 3D exploitation of imagery in the fields of Computer vision and Photogrammetry. Although numerous solutions and advances have been proposed in the literature, occlusions, disparity discontinuities, sparse texture, image distortion, and illumination changes still lead to problematic issues and await better treatment. In this paper, a hybrid-based method based on semi-global matching is presented to tackle the challenges on dense stereo matching. To ease the sensitiveness of SGM cost aggregation towards penalty parameters, a formal way to provide proper penalty estimates is proposed. To this end, the study manipulates a shape-adaptive cross-based matching with an edge constraint to generate an initial disparity map for penalty estimation. Image edges, indicating the potential locations of occlusions as well as disparity discontinuities, are approved by the edge drawing algorithm to ensure the local support regions not to cover significant disparity changes. Besides, an additional penalty parameter 𝑃𝑒 is imposed onto the energy function of SGM cost aggregation to specifically handle edge pixels. Furthermore, the final disparities of edge pixels are found by weighting both values derived from the SGM cost aggregation and the U-SURF matching, providing more reliable estimates at disparity discontinuity areas. Evaluations on Middlebury stereo benchmarks demonstrate satisfactory performance and reveal the potency of the hybrid-based dense stereo matching method.

scholarly journals HYBRID-BASED DENSE STEREO MATCHING

2016 ◽  
Vol XLI-B3 ◽  
pp. 495-501
Author(s):  
T. Y. Chuang ◽  
H. W. Ting ◽  
J. J. Jaw
Keyword(s):  
Stereo Matching ◽  
Penalty Parameter ◽  
Disparity Map ◽  
Cost Aggregation ◽  
Satisfactory Performance ◽  
Dense Stereo Matching ◽  
Illumination Changes ◽  
Global Matching ◽  
Disparity Maps ◽  
Image Edges

Stereo matching generating accurate and dense disparity maps is an indispensable technique for 3D exploitation of imagery in the fields of Computer vision and Photogrammetry. Although numerous solutions and advances have been proposed in the literature, occlusions, disparity discontinuities, sparse texture, image distortion, and illumination changes still lead to problematic issues and await better treatment. In this paper, a hybrid-based method based on semi-global matching is presented to tackle the challenges on dense stereo matching. To ease the sensitiveness of SGM cost aggregation towards penalty parameters, a formal way to provide proper penalty estimates is proposed. To this end, the study manipulates a shape-adaptive cross-based matching with an edge constraint to generate an initial disparity map for penalty estimation. Image edges, indicating the potential locations of occlusions as well as disparity discontinuities, are approved by the edge drawing algorithm to ensure the local support regions not to cover significant disparity changes. Besides, an additional penalty parameter 𝑃𝑒 is imposed onto the energy function of SGM cost aggregation to specifically handle edge pixels. Furthermore, the final disparities of edge pixels are found by weighting both values derived from the SGM cost aggregation and the U-SURF matching, providing more reliable estimates at disparity discontinuity areas. Evaluations on Middlebury stereo benchmarks demonstrate satisfactory performance and reveal the potency of the hybrid-based dense stereo matching method.

scholarly journals AMBIGUITY CONCEPT IN STEREO MATCHING PIPELINE

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 383-390
Author(s):  
E. Sarrazin ◽  
M. Cournet ◽  
L. Dumas ◽  
V. Defonte ◽  
Q. Fardet ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Performance Estimation ◽  
Cost Curve ◽  
Disparity Map ◽  
Integral Measure ◽  
Global Matching ◽  
Good Potential ◽  
The Cost ◽  
The Impact

Abstract. In a 3D reconstruction pipeline, stereo matching step aims at computing a disparity map representing the depth between image pair. The evaluation of the disparity map can be done through the estimation of a confidence metric. In this article, we propose a new confidence metric, named ambiguity integral metric, to assess the quality of the produced disparity map. This metric is derived from the concept of ambiguity, which characterizes the property of the cost curve profile. It aims to quantify the difficulty in identifying the correct disparity to select. The quality of ambiguity integral metric is evaluated through the ROC curve methodology and compared with other confidence measures. In regards to other measures, the ambiguity integral measure shows a good potential. We also integrate this measure through various steps of the stereo matching pipeline in order to improve the performance estimation of the disparity map. First, we include ambiguity integral measure during the Semi Global Matching optimization step. The objective is to weight, by ambiguity integral measure, the influence of points in the SGM regularization to reduce the impact of ambiguous points. Secondly, we use ambiguity as an input of a disparity refinement deep learning architecture in order to easily locate noisy area and preserve details.

scholarly journals QUASI-EPIPOLAR RESAMPLING OF HIGH RESOLUTION SATELLITE STEREO IMAGERY FOR SEMI GLOBAL MATCHING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 707-712 ◽  
Author(s):  
N. Tatar ◽  
M. Saadatseresht ◽  
H. Arefi ◽  
A. Hadavand
Keyword(s):  
Stereo Matching ◽  
Stereo Pair ◽  
Matching Algorithm ◽  
Automatic Feature Extraction ◽  
Polynomial Coefficients ◽  
Sensor Model ◽  
Rational Polynomial ◽  
Straight Line ◽  
Stereo Imagery ◽  
Global Matching

Semi-global matching is a well-known stereo matching algorithm in photogrammetric and computer vision society. Epipolar images are supposed as input of this algorithm. Epipolar geometry of linear array scanners is not a straight line as in case of frame camera. Traditional epipolar resampling algorithms demands for rational polynomial coefficients (RPCs), physical sensor model or ground control points. In this paper we propose a new solution for epipolar resampling method which works without the need for these information. In proposed method, automatic feature extraction algorithms are employed to generate corresponding features for registering stereo pairs. Also original images are divided into small tiles. In this way by omitting the need for extra information, the speed of matching algorithm increased and the need for high temporal memory decreased. Our experiments on GeoEye-1 stereo pair captured over Qom city in Iran demonstrates that the epipolar images are generated with sub-pixel accuracy.

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