On the asymptotics of minimum disparity estimation

Test ◽  
2016 ◽  
Vol 26 (3) ◽  
pp. 481-502 ◽  
Author(s):  
Arun Kumar Kuchibhotla ◽  
Ayanendranath Basu
Keyword(s):  
Disparity Estimation

Fast disparity estimation algorithm based on features of disparity vector

2013 ◽  
Vol 32 (6) ◽  
pp. 1856-1859
Author(s):  
Xiao-wei SONG ◽  
Lei YANG ◽  
Zhong LIU ◽  
Liang LIAO
Keyword(s):  
Estimation Algorithm ◽  
Disparity Estimation ◽  
Disparity Vector

Efficient disparity estimation from stereo images using hybrid-guided image filter

2017 ◽  
Vol 66 (3) ◽  
pp. 139-151
Author(s):  
Khushboo Jain ◽  
Husanbir Singh Pannu ◽  
Kuldeep Singh ◽  
Avleen Malhi
Keyword(s):  
Disparity Estimation ◽  
Stereo Images ◽  
Image Filter ◽  
Guided Image Filter

Improved Disparity Estimation Algorithm Based on PSMNet

2021 ◽  
Author(s):  
Juan Du ◽  
Yongchao Tang ◽  
Bohang Li ◽  
Dengping Lin ◽  
Juan Huang
Keyword(s):  
Estimation Algorithm ◽  
Disparity Estimation

Binocular and Trinocular Disparity Estimation

2017 ◽  
pp. 167-198
Author(s):  
Vladan Popovic ◽  
Kerem Seyid ◽  
Ömer Cogal ◽  
Abdulkadir Akin ◽  
Yusuf Leblebici
Keyword(s):  
Disparity Estimation

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.

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