Depth Image Super Resolution for 3D Reconstruction of Oil Reflnery Buildings

Li, Shuaihao; Zhang, Bin; Yang, Xi; He, Yanxiang; Chen, Min

doi:10.1007/s10553-019-01055-z

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…As a major noise type in depth map, Gaussian noise has been widely concerned by researchers. Consequently effective denoising methods have appeared, such as, filtering-based methods [1][2][3] , partial differential equation (PDE)-based methods [4][5][6] , sparse representation-based dictionary learning methods [7][8][9][10][11] , deep learning-based methods [12][13][14][15][16][17][18][19][20] , and recent variation minimization-based methods [21][22][23][24] .…”

Section: Edge-guided Second-order Total Generalized Variation For Gaumentioning

confidence: 99%

Edge-guided second-order total generalized variation for Gaussian noise removal from depth map

Zhang

Yang

et al. 2020

Sci Rep

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Total generalized variation models have recently demonstrated high-quality denoising capacity for single image. In this paper, we present an accurate denoising method for depth map. Our method uses a weighted second-order total generalized variational model for Gaussian noise removal. By fusing an edge indicator function into the regularization term of the second-order total generalized variational model to guide the diffusion of gradients, our method aims to use the first or second derivative to enhance the intensity of the diffusion tensor. We use the first-order primal–dual algorithm to minimize the proposed energy function and achieve high-quality denoising and edge preserving result for depth maps with high -intensity noise. Extensive quantitative and qualitative evaluations in comparison to bench-mark datasets show that the proposed method provides significant higher accuracy and visual improvements than many state-of-the-art denoising algorithms.

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Section: Edge-guided Second-order Total Generalized Variation For Gaumentioning

confidence: 99%

Edge-guided second-order total generalized variation for Gaussian noise removal from depth map

Zhang

Yang

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

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3D Real-Time Modeling Based on Lidar for Circular Coal Bunker of Coal-Fired Enterprise

Huang

Zhao

et al. 2022

Chem Technol Fuels Oils

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Data-Driven 3d Effect Enhancement Mechanism of Watercolor: A Neural Network-Based Approach

Zhang

2023

Fractals

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A watercolor is an art form that uses water to express the painting process. Water is the specific boundary that distinguishes watercolor from other painting types. Fluidity is one of the essential factors constituting its distinctive artistic beauty. With vibrant hues and delicate brushstrokes, 3D watercolors enhance the image’s content with more meaningful information. It has more visual impact than a watercolor that is flat. Appreciators can visualize the scenario in their minds, which leaves more room for imagination and is particularly useful for usage in animation. This paper uses 3D point cloud reconstruction to enhance the 3D effect of watercolor and proposes a 3D reconstruction method of the point cloud based on a generative adversarial network. First, a new mesh model is obtained by using a predictor to predict the offset of the mesh model edge. Then, the point cloud classifier in the discriminator is used to extract the high dimensional features of the original point cloud data and the surface sampling point set of the mesh model. Finally, the output data of the predictor and discriminator are associated with the adversarial training method, and the network model is optimized through several iterations to obtain a 3D mesh model satisfying the spatial characteristics of the point cloud. The experimental results demonstrate that the method suggested in this paper is superior to the three benchmarks in terms of watercolor’s texture, color transition, highlight part, and iteration time, as well as its ability to enhance the 3D effect.

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Depth Image Super Resolution for 3D Reconstruction of Oil Reflnery Buildings

Cited by 3 publications

References 13 publications

Edge-guided second-order total generalized variation for Gaussian noise removal from depth map

Edge-guided second-order total generalized variation for Gaussian noise removal from depth map

3D Real-Time Modeling Based on Lidar for Circular Coal Bunker of Coal-Fired Enterprise

Data-Driven 3d Effect Enhancement Mechanism of Watercolor: A Neural Network-Based Approach

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