Stereoview to Multiview Conversion Architecture for Auto-Stereoscopic 3D Displays

Kuo, P. K.; Lo, Kuan-Lun; Tseng, Huan-Kai; Lee, Kuan‐Ting; Liu, Bin-Da; Yang, Jar‐Ferr

doi:10.1109/tcsvt.2017.2732061

Cited by 8 publications

(5 citation statements)

References 48 publications

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“…Table 3 shows the quantitative results between the four‐mode census [8], TCC census [7], auto‐stereoscopic 3D displays [34], variable window size with edge [35], and the proposed stereo matching method. With a simple refinement stated in Subsection 3.4, the proposed method mostly shows better quantitative results of the percentage of bad pixels (2‐pixel error) in the non‐occlusion region (Non), all region (All), discontinuous region (Disc) and the average percentage error (Avg.…”

Section: Resultsmentioning

confidence: 99%

Improved quadruple sparse census transform and adaptive multi‐shape aggregation algorithms for precise stereo matching

Huang

Yang

2021

IET Computer Vision

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Stereo matching is an important computer vision tool for three-dimensional (3D) positioning, 3D visual representation, precision detection and recognition with 3D information. For 3D visual representation, for example, the depth image-based rendering (DIBR) needs high accuracy depth maps to synthesise multiple views for 3D naked-eyes displays. It is hard to predict high-precision disparity for smooth regions and occlusion areas by traditional census-based stereo matching methods. In this work, two new improved algorithms, called the improved quadruple sparse census transform and the adaptive multi-shape aggregation, are proposed to achieve a precise stereo matching system. Instead of a binary threshold, the improved quadruple auxiliary census function with an adaptive threshold and the patch mean can raise the discrimination performance for better stereo matching. The selected sparse census patch is proposed to reduce the computation of the pixel-matching cost. For effective aggregation of matching costs, multi-shape windows and a texture-aware algorithm is suggested to decide a suitable window. The proposed adaptive multi-shape aggregation method performs better and solves the discontinuous depth problem occurring near the object boundaries. Experimental results show that the proposed stereo matching system with the proposed quadruple sparse census transform and multi-shape cost aggregation achieves better raw depth maps than the existing algorithms for stereo matching.

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Section: Resultsmentioning

confidence: 99%

Improved quadruple sparse census transform and adaptive multi‐shape aggregation algorithms for precise stereo matching

Huang

Yang

2021

IET Computer Vision

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show abstract

“…15 Estimated disparity maps of Cones achieved by a Kuo et al [27], b Kuo [28], c Hsieh [29], d Sun [31], e ASW [21], f S-ASW [23], g LF-SM [36], and h the proposed method Fig. 16 Estimated disparity maps of Teddy achieved by a Kuo et al [27], b Kuo [28], c Hsieh [29], d Sun [31], e ASW [21], f S-ASW [23], g LF-SM [36], and h the proposed methods adaptive cost selection, the segment-based adaptive weights for cost aggregation, twolevel WTA strategy, and dual-path depth refinement. For small holes, the depth refinement uses maximum-weighted candidate for the best filling process.…”

Section: Discussionmentioning

confidence: 99%

“…where d i and d r are the disparities of the left and right views respectively, and σ 0 is the tolerance for detecting the wrong disparity. To correct the mismatched pixels with L(x, y) = 0, there are several disparity refinement methods [26][27][28][29][30][31]. Usually, we can classify the mismatching pixels into large and small hole regions.…”

Section: Disparity Refinementmentioning

confidence: 99%

Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

Huang

Chan

et al. 2020

J Image Video Proc.

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Stereo matching is one of the most important topics in computer vision and aims at generating precise depth maps for various applications. The major challenge of stereo matching is to suppress inevitable errors occurring in smooth, occluded, and discontinuous regions. In this paper, the proposed stereo matching system uses segment-based superpixels and matching cost. After determination of edge and smooth regions and selection of matching cost, we suggest the segment-based adaptive support weights in cost aggregation instead of color similarity and spatial proximity only. The proposed dual-path depth refinements use the cross-based support region by referring texture features to correct the inaccurate disparities with iterative procedures to improve the depth maps for shape reserving. Specially for leftmost and rightmost regions, the segment-based refinement can greatly improve the mismatched disparity holes. The experimental results show that the proposed system can achieve higher accurate depth maps than the conventional methods.

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“…The EIA may contain holes because the RGBD image pair provides perspective from only a single viewpoint, which leads to the degradation of reconstructed 3D images. Conventional hole-filling algorithms based on neighborhood interpolation [35] can alleviate this issue to some content, but they may cause image blurs, especially when dealing with large depth-of-field images. In our method, the holes are filled by extending the texture slices with specific pixels.…”

Section: Generation Of Eia With Optimum Voxel Spacementioning

confidence: 99%

Integral Imaging-Based Light Field Display System With Optimum Voxel Space

Liu,

Li,

Deng

2024

IEEE Photonics J.

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Abstract-Integral imaging (InIm) based light field display (LFD) is still faced with challenges in the complexity of light field data acquisition and generation and unsatisfactory display effects. This paper presents an InIm-based LFD system with an optimum voxel space. The study's main innovation lies in two aspects: Firstly, it reveals the intrinsic voxel spatial distribution of the InIm-based LFD and analyzes how the voxel space affects the display performances such as spatial resolution, depth of field, and smoothness of parallax. Secondly, it proposes a method to generate an elemental image array (EIA) from a pair of RGB and depth (RGBD) images based on the optimally selected voxel space.In the experiments, we tested the display performances of the voxels on different depth planes and obtained results consistent with the theoretical analyses. We also experimented with a computer 3D model and a real-world scene on two InIm-based LFD prototypes working in different modes, one in real mode and the other in virtual mode, and obtained favorable 3D display effects. The proposed system has a simple and compact hardware structure and has shown flexibility and scalability in the aspect of light field data acquisition. Also, it applies to various 3D scenes, either virtual models or realistic objects. We expect the proposed system to help the practical application of InIm-based LFD technology.

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Stereoview to Multiview Conversion Architecture for Auto-Stereoscopic 3D Displays

Cited by 8 publications

References 48 publications

Improved quadruple sparse census transform and adaptive multi‐shape aggregation algorithms for precise stereo matching

Improved quadruple sparse census transform and adaptive multi‐shape aggregation algorithms for precise stereo matching

Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

Integral Imaging-Based Light Field Display System With Optimum Voxel Space

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