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Article

Enhanced Soft 3D Reconstruction Method with an Iterative Matching Cost Update Using Object Surface Consensus

1
Graduate School of Electronics and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
2
Digital Broadcasting Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Denis Laurendeau
Sensors 2021, 21(19), 6680; https://doi.org/10.3390/s21196680
Received: 27 July 2021 / Revised: 27 September 2021 / Accepted: 4 October 2021 / Published: 8 October 2021
(This article belongs to the Special Issue Sensing and Processing for 3D Computer Vision)
In this paper, we propose a multi-view stereo matching method, EnSoft3D (Enhanced Soft 3D Reconstruction) to obtain dense and high-quality depth images. Multi-view stereo is one of the high-interest research areas and has wide applications. Motivated by the Soft3D reconstruction method, we introduce a new multi-view stereo matching scheme. The original Soft3D method is introduced for novel view synthesis, while occlusion-aware depth is also reconstructed by integrating the matching costs of the Plane Sweep Stereo (PSS) and soft visibility volumes. However, the Soft3D method has an inherent limitation because the erroneous PSS matching costs are not updated. To overcome this limitation, the proposed scheme introduces an update process of the PSS matching costs. From the object surface consensus volume, an inverse consensus kernel is derived, and the PSS matching costs are iteratively updated using the kernel. The proposed EnSoft3D method reconstructs a highly accurate 3D depth image because both the multi-view matching cost and soft visibility are updated simultaneously. The performance of the proposed method is evaluated by using structured and unstructured benchmark datasets. Disparity error is measured to verify 3D reconstruction accuracy, and both PSNR and SSIM are measured to verify the simultaneous enhancement of view synthesis. View Full-Text
Keywords: stereo vision; multi-view stereo matching; iterative; refinement; view synthesis stereo vision; multi-view stereo matching; iterative; refinement; view synthesis
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MDPI and ACS Style

Lee, M.-J.; Um, G.-M.; Yun, J.; Cheong, W.-S.; Park, S.-Y. Enhanced Soft 3D Reconstruction Method with an Iterative Matching Cost Update Using Object Surface Consensus. Sensors 2021, 21, 6680. https://doi.org/10.3390/s21196680

AMA Style

Lee M-J, Um G-M, Yun J, Cheong W-S, Park S-Y. Enhanced Soft 3D Reconstruction Method with an Iterative Matching Cost Update Using Object Surface Consensus. Sensors. 2021; 21(19):6680. https://doi.org/10.3390/s21196680

Chicago/Turabian Style

Lee, Min-Jae, Gi-Mun Um, Joungil Yun, Won-Sik Cheong, and Soon-Yong Park. 2021. "Enhanced Soft 3D Reconstruction Method with an Iterative Matching Cost Update Using Object Surface Consensus" Sensors 21, no. 19: 6680. https://doi.org/10.3390/s21196680

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