Depth-guided learning light field angular super-resolution with edge-aware inpainting

Liu, Xia; Wang, Minghui; Wang, Anzhi; Hua, Xiyao; Liu, Shanshan

doi:10.1007/s00371-021-02159-6

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…The sparse inputs within the LF′s aperture usually require fixed input subaperture positions, e.g., four corner views in [5]. So, FlexLF [11] was proposed for LF synthesis with sparse input SAIs in varying aperture positions. The angular correlations among SAIs are revealed by building a cost volume to calculate pixel intensity matching errors.…”

Section: Lf Synthesis Based On Sparse Angular Referencesmentioning

confidence: 99%

Robust Local Light Field Synthesis via Occlusion-aware Sampling and Deep Visual Feature Fusion

2023

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Novel view synthesis has attracted tremendous research attention recently for its applications in virtual reality and immersive telepresence. Rendering a locally immersive light field (LF) based on arbitrary large baseline RGB references is a challenging problem that lacks efficient solutions with existing novel view synthesis techniques. In this work, we aim at truthfully rendering local immersive novel views/LF images based on large baseline LF captures and a single RGB image in the target view. To fully explore the precious information from source LF captures, we propose a novel occlusion-aware source sampler (OSS) module which efficiently transfers the pixels of source views to the target view’s frustum in an occlusion-aware manner. An attention-based deep visual fusion module is proposed to fuse the revealed occluded background content with a preliminary LF into a final refined LF. The proposed source sampling and fusion mechanism not only helps to provide information for occluded regions from varying observation angles, but also proves to be able to effectively enhance the visual rendering quality. Experimental results show that our proposed method is able to render high-quality LF images/novel views with sparse RGB references and outperforms state-of-the-art LF rendering and novel view synthesis methods.

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Section: Lf Synthesis Based On Sparse Angular Referencesmentioning

confidence: 99%

Robust Local Light Field Synthesis via Occlusion-aware Sampling and Deep Visual Feature Fusion

2023

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“…Numerous post-processing applications [4], [8] utilizing LF data rely on disparity information [10] of the scene, e.g. view synthesis [11] and super-resolution [12]. In recent studies, many deep learning (DL) based algorithms are proposed and have achieved significant improvement in the estimation of disparity information [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Light-Weight EPINET Architecture for Fast Light Field Disparity Estimation

Hassan

Sjöström

Zhang

et al. 2022

2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)

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Recent deep learning-based light field disparity estimation algorithms require millions of parameters, which demand high computational cost and limit the model deployment. In this paper, an investigation is carried out to analyze the effect of depthwise separable convolution and ghost modules on stateof-the-art EPINET architecture for disparity estimation. Based on this investigation, four convolutional blocks are proposed to make the EPINET architecture a fast and light-weight network for disparity estimation. The experimental results exhibit that the proposed convolutional blocks have significantly reduced the computational cost of EPINET architecture by up to a factor of 3.89, while achieving comparable disparity maps on HCI Benchmark dataset.

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