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View synthesis

About: View synthesis is a research topic. Over the lifetime, 1701 publications have been published within this topic receiving 42333 citations.


Papers
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Journal ArticleDOI
TL;DR: This paper introduces a novel and efficient depth- based texture coding scheme that includes depth-based motion vector prediction, block-based view synthesis prediction, and adaptive luminance compensation, which were adopted in an AVC-compatible 3D video coding standard.
Abstract: The target of 3D video coding is to compress Multiview Video plus Depth (MVD) format data, which consist of a texture image and its corresponding depth map. In the MVD format, the depth map plays an important role for successful services in 3D video applications, because it enables the user to experience 3D by generating arbitrary intermediate views. The depth map has a strong correlation with its associated texture data, so it can be utilized to improve texture coding efficiency. This paper introduces a novel and efficient depth-based texture coding scheme. It includes depth-based motion vector prediction, block-based view synthesis prediction, and adaptive luminance compensation, which were adopted in an AVC-compatible 3D video coding standard. Simulation results demonstrate that the proposed scheme reduces the total coding bitrates of texture and depth by 19.06% for the coded PSNR and 17.01% for the synthesized PSNR in a P-I-P view prediction structure, respectively.

8 citations

Proceedings ArticleDOI
01 Sep 2017
TL;DR: This work reformulates the view synthesis task as an image reconstruction problem with a spatial transformer module and directly make stereo image pairs with a unified CNN framework without ground-truth depth as a supervision to capture the large displacements between images from coarse-level and enhance the detail from fine-level.
Abstract: We present a multi-scale deep convolutional neural network (CNN) for the task of automatic 2D-to-3D conversion. Traditional methods, which make a virtual view from a reference view, consist of separate stages i.e., depth (or disparity) estimation for the reference image and depth image-based rendering (DIBR) with estimated depth. In contrast, we reformulate the view synthesis task as an image reconstruction problem with a spatial transformer module and directly make stereo image pairs with a unified CNN framework without ground-truth depth as a supervision. We further propose a multi-scale deep architecture to capture the large displacements between images from coarse-level and enhance the detail from fine-level. Experimental results demonstrate the effectiveness of the proposed method over state-of-the-art approaches both qualitatively and quantitatively on the KITTI driving dataset.

8 citations

Proceedings Article
12 Aug 2021
TL;DR: In this article, an approach that fuses 3D reasoning with autoregressive modeling is presented to outpaint large view changes in a 3D-consistent manner, enabling scene synthesis.
Abstract: Recent advancements in differentiable rendering and 3D reasoning have driven exciting results in novel view synthesis from a single image. Despite realistic results, methods are limited to relatively small view change. In order to synthesize immersive scenes, models must also be able to extrapolate. We present an approach that fuses 3D reasoning with autoregressive modeling to outpaint large view changes in a 3D-consistent manner, enabling scene synthesis. We demonstrate considerable improvement in single image large-angle view synthesis results compared to a variety of methods and possible variants across simulated and real datasets. In addition, we show increased 3D consistency compared to alternative accumulation methods. Project website: this https URL

8 citations

Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper proposed a view synthesis approach based on view space covering to deal with the challenge of large-angle interval for long-distance human recognition, which can be obtained by aligning human silhouettes and averaging them in a gait cycle.

8 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202354
2022117
2021189
2020158
2019114
2018102