On consistent inter-view synthesis for autostereoscopic displays

Tran, L.T.; Bal, Can; Pal, Christopher; Nguyen, Truong Q.

doi:10.1007/3dres.01(2012)1

Cited by 11 publications

(1 citation statement)

References 17 publications

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“…The PSNR and SSIM measurements of all nine test images are shown in Table 3 . For a sufficient number of comparisons, we use six algorithms, including Jain et al [ 50 ], Tran et al [ 51 ], Ramachandran et al [ 52 ] and Soft3D. In our results, the PSNR ranges from 38 to 45 and the SSIM ranges from 0.97 to 0.99, which are the highest scores among all the compared methods.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

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

Lee

Yun

et al. 2021

Sensors

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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.

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Section: Experiments and Evaluationmentioning

confidence: 99%