Results 11 to 20 of about 171,197 (277)
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 For modeling the 3D world behind 2D images, which 3D representation is most appropriate? A polygon mesh is a promising candidate for its compactness and geometric properties. However, it is not straightforward to model a polygon mesh from 2D images using
Harada, Tatsuya +2 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Project page with code and videos at https://light-field-neural-rendering.github ...
Mohammed Suhail +3 more
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Neural Scene De-rendering [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 We study the problem of holistic scene understanding. We would like to obtain a compact, expressive, and interpretable representation of scenes that encodes information such as the number of objects and their categories, poses, positions, etc. Such a representation would allow us to reason about and even reconstruct or manipulate elements of the scene.
Wu, Jiajun +2 more
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BokehMe: When Neural Rendering Meets Classical Rendering
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 We propose BokehMe, a hybrid bokeh rendering framework that marries a neural renderer with a classical physically motivated renderer. Given a single image and a potentially imperfect disparity map, BokehMe generates high-resolution photo-realistic bokeh effects with adjustable blur size, focal plane, and aperture shape.
Peng, Juewen +5 more
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, 2022 Recent advances in deep learning techniques and applications have revolutionized artistic creation and manipulation in many domains (text, images, music); however, fonts have not yet been integrated with deep learning architectures in a manner that supports their multi-scale nature.
Anderson, Daniel +2 more
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Computer Graphics Forum, 2022 AbstractSynthesizing photo‐realistic images and videos is at the heart of computer graphics and has been the focus of decades of research. Traditionally, synthetic images of a scene are generated using rendering algorithms such as rasterization or ray tracing, which take specifically defined representations of geometry and material properties as input.
Tewari, A. +16 more
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Stochasticity from function -- why the Bayesian brain may need no noise [PDF]
, 2019 An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing.
Baumbach, Andreas +8 more
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Unsupervised Neural Rendering for Image Hazing
IEEE Transactions on Image Processing, 2022 Image hazing aims to render a hazy image from a given clean one, which could be applied to a variety of practical applications such as gaming, filming, photographic filtering, and image dehazing. To generate plausible haze, we study two less-touched but challenging problems in hazy image rendering, namely, i) how to estimate the transmission map from a
Boyun Li +5 more
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Modeling the Biocatalytic Method of Lipid Extraction Using Artificial Neural Networks
Information, 2023 The paper presents the data on lipid fraction extraction from the raw fat of hibernating hunting animals. The processing of valuable raw materials must be maximized. For this purpose, various methods of rendering are used.
Anton V. Shafrai +2 more
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Texture synthesis of ecological plant protection image based on convolution neural network
Frontiers in Plant Science, 2022 Texture synthesis technology is an important realistic rendering technology. Texture synthesis technology also has a good application prospect in image rendering and other fields. Convolutional neural network is a very popular technology in recent years.
Libing Hu, Fei Zhou, Xianjun Fu
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