Voxel-based three-view hybrid parallel network for 3D object classification

Deep learning for image inpainting: A survey

A brief survey on RGB-D semantic segmentation using deep learning

Automatic object removal with obstructed facades completion in the urban environment is essential for many applications such as scene restoration, environmental impact assessment, and urban mapping. However, the previous object removal typically requires a user to manually create a mask around unwanted objects and obtain background facade information in advance, which would be labor-intensive when implementing multitasking projects. Moreover, accurately detecting objects to be removed in the cityscape and inpainting the static obstructed building facade to obtain plausible images are the main challenges for this objective. To overcome these difficulties, this study addresses the object removal with the facade inpainting problem from the following two aspects. First, we proposed an image-based cityscape elimination method for automatic object removal and facade inpainting by applying semantic segmentation to detect several classes, including pedestrians, riders, vegetation, and cars, as well as using generative adversarial networks (GANs) for filling detected regions by background textures and patching information from street-level imagery. Second, we proposed a workflow to filter unoccluded building facades from street view images automatically and tailored a dataset for the GAN-based image inpainting model with original and mask images. Furthermore, several full-reference image quality assessment (IQA) metrics are introduced to evaluate the generated image quality. Validation results demonstrated the feasibility and effectiveness of our proposed method, and the synthetic image is visually realistic and semantically consistent.

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Automatic Object Removal With Obstructed Façades Completion Using Semantic Segmentation and Generative Adversarial Inpainting

Learning operators between infinitely dimensional spaces is an important learning task arising in wide applications in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

Qingliang Zeng

Papers

Image inpainting based on deep learning: A review