Efficient Hardware Architecture for Large Disparity Range Stereo Matching Based on Belief Propagation

2016 ◽  
Author(s):  
Sih-Sian Wu ◽  
Chen-Han Tsai ◽  
Liang-Gee Chen
Keyword(s):  
Stereo Matching ◽  
Belief Propagation ◽  
Hardware Architecture ◽  
Disparity Range

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.

Stereo Matching with Semi-limited Belief Propagation

2012 ◽  
Author(s):  
Chengzhi Luo ◽  
Jianjun Lei ◽  
Guanglong Hu ◽  
Kefeng Fan ◽  
Shupo Bu
Keyword(s):  
Stereo Matching ◽  
Belief Propagation

A new stereo matching method with combination of cross-based aggregation and hierarchical belief propagation

2012 ◽  
Author(s):  
Tianyu Yang ◽  
Qing He ◽  
Ning Wei ◽  
Baopu Li ◽  
Chao Hu ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Belief Propagation ◽  
Matching Method

scholarly journals An Edge-Sense Bidirectional Pyramid Network for Stereo Matching of VHR Remote Sensing Images

2020 ◽  
Vol 12 (24) ◽  
pp. 4025
Author(s):  
Rongshu Tao ◽  
Yuming Xiang ◽  
Hongjian You
Keyword(s):  
Remote Sensing ◽  
Stereo Matching ◽  
Tall Buildings ◽  
Disparity Estimation ◽  
Complex Structures ◽  
Learning Networks ◽  
Remote Sensing Images ◽  
Essential Step ◽  
Disparity Range ◽  
The Cost

As an essential step in 3D reconstruction, stereo matching still faces unignorable problems due to the high resolution and complex structures of remote sensing images. Especially in occluded areas of tall buildings and textureless areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we develop a novel edge-sense bidirectional pyramid stereo matching network to solve the aforementioned problems. The cost volume is constructed from negative to positive disparities since the disparity range in remote sensing images varies greatly and traditional deep learning networks only work well for positive disparities. Then, the occlusion-aware maps based on the forward-backward consistency assumption are applied to reduce the influence of the occluded area. Moreover, we design an edge-sense smoothness loss to improve the performance of textureless areas while maintaining the main structure. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two methods, DenseMapNet and PSMNet, in terms of averaged endpoint error (EPE) and the fraction of erroneous pixels (D1), and the improvements in occluded and textureless areas are significant.

Stereo matching using belief propagation

2003 ◽  
Vol 25 (7) ◽  
pp. 787-800 ◽  
Author(s):  
Jian Sun ◽  
Nan-Ning Zheng ◽  
Heung-Yeung Shum
Keyword(s):  
Stereo Matching ◽  
Belief Propagation

Real-time stereo matching using memory-efficient Belief Propagation for high-definition 3D telepresence systems

2011 ◽  
Vol 32 (16) ◽  
pp. 2250-2253 ◽  
Author(s):  
Jesús M. Pérez ◽  
Pablo Sánchez
Keyword(s):  
Real Time ◽  
Stereo Matching ◽  
Belief Propagation ◽  
High Definition ◽  
Memory Efficient

Belief propagation stereo matching algorithm for Chang’E-3 pancam image data

2016 ◽  
Vol 46 (2) ◽  
pp. 029606
Author(s):  
FangFang CHEN ◽  
Yan SONG ◽  
YuGang TIAN ◽  
DongBo ZHU
Keyword(s):  
Stereo Matching ◽  
Belief Propagation ◽  
Image Data ◽  
Matching Algorithm

Ambiguous Region Matching and Boundary Pixel Optimization for Stereo Computation

2012 ◽  
Vol 546-547 ◽  
pp. 735-740
Author(s):  
Xing Nian Cui ◽  
Fan Yang ◽  
Qing Min Liao
Keyword(s):  
Stereo Matching ◽  
Belief Propagation ◽  
Mean Shift ◽  
Mixed Pixel ◽  
Region Matching ◽  
Mixed Pixels ◽  
Belief Propagation Algorithm ◽  
The Mean ◽  
Boundary Pixel ◽  
Coarse To Fine

In this paper, we present a stereo matching algorithm based on planar surface hypothesis. It improves the results of low texture regions and mixed pixels on object boundaries. First, regions are segmented by applying the mean-shift segmentation method. Then we propose a coarse-to-fine algorithm to increase the reliable correspondences in low texture regions. Third, the Belief Propagation algorithm is used to optimize disparity plane labeling. Finally, for a mixed pixel, we utilize the results of the depth plane and the local region of it to regulate its disparity. Experimental results using the Middlebury stereo test show that the performance of our method is high.

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