A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Filters
Deep Shading: Convolutional Neural Networks for Screen Space Shading
2017
Computer graphics forum (Print)
Preserved Fulltext
Other Versions
In computer vision, convolutional neural networks (CNNs) have recently achieved new levels of performance for several inverse problems where RGB pixel appearance is mapped to attributes such as positions ...
The resulting Deep Shading simulates various screen-space effects at competitive quality and speed while not being programmed by human experts but learned from example images. ...
(Convolutional) Neural Networks Neural networks are a way of defining powerful non-linear approximations f . ...
doi:10.1111/cgf.13225
fatcat:goqmnw7xwjdcbernfyg46is34u
DeepAO: Efficient Screen Space Ambient Occlusion Generation via Deep Network
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Then, we design an efficient deep neural network for the screen space AO image generation, based on which we further design a Compute Shader Library to compute the shaded AO images. ...
INDEX TERMS Ambient occlusion, rendering, shading, deep neural network. ...
[12] introduce a convolution neural network named Deep Shading to learn a mapping from position in camera space and normal to AO shaded image. ...
doi:10.1109/access.2020.2984771
fatcat:ji6von45gramfoi3ehq27ce36e
Deep-learning the Latent Space of Light Transport
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
While many previous learning methods have employed 2D convolutional neural networks applied to images, we show for the first time that light transport can be learned directly in 3D. ...
We suggest a method to directly deep-learn light transport, i. e., the mapping from a 3D geometry-illumination-material configuration to a shaded 2D image. ...
We would like to acknowledge the NVIDIA Corporation for donating a Quadro P6000 for our training cluster, and Gloria Fackelmann for providing the voice over the supplementary video. ...
arXiv:1811.04756v2
fatcat:cjpkvlradra7lf5bma7wfjmnce
Plausible Shading Decomposition For Layered Photo Retouching
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We perform such a decomposition by learning a convolutional neural network trained using synthetic data. ...
shading. ...
The occlusion term O is computed using screen-space occlusion [Ritschel et al. 2009 ]. ...
arXiv:1701.06507v2
fatcat:oowoduhk7jg4fgx7uetxpvpjeq
Image Colour Prediction using Deep Learning
2020
International journal of recent technology and engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Any image we perceive through a screen is made of three separate channels, R, G, and B. With the help of these three channels; an image comes to colour. ...
Generative adversarial networks are an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input file to get an output. ...
We are using the Convolution Neural Framework fit for concealing profoundly differentiating pictures. ...
doi:10.35940/ijrte.e5935.038620
fatcat:5kj2gu34hrdanlff7vqkg4lmnm
Object Classification and Detection using Deep Convolution Neural Network Architecture
2020
International journal of recent technology and engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
A solid programmed disallowed thing location framework is subsequently ideal for accelerating the screening procedure just as improving the precision of risk identification. ...
X-Ray security stuff screening frameworks are generally introduced in pretty much every station/air terminal to guarantee open vehicle security. ...
This positively is useful for Convolutional Neural Network model preparation.
II. RELATED WORK Deep convolutional neural network (DCNN) improvements have two expressions. ...
doi:10.35940/ijrte.f9834.059120
fatcat:6egijwjlvbaltm3kke7p5fjjlq
Deep Photon Mapping
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We train a novel deep neural network to predict a kernel function to aggregate photon contributions at shading points. ...
In this paper, we develop the first deep learning-based method for particle-based rendering, and specifically focus on photon density estimation, the core of all particle-based methods. ...
in both scene-space particle density estimation and screen-space MC integration. ...
arXiv:2004.12069v1
fatcat:fias4l6d5ngdnlxhtgsmwhboke
Deep Reinforcement Learning for Pellet Eating in Agar.io
2019
Proceedings of the 11th International Conference on Agents and Artificial Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The architectures examined are two convolutional Deep Q-networks (DQN) of varying depth and one multilayer perceptron. ...
This work first investigates how different state representations affect the learning process of a Q-learning algorithm combined with artificial neural networks which are used for representing the Q-function ...
ACKNOWLEDGMENTS We would like to thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Peregrine high performance computing cluster ...
doi:10.5220/0007360901230133
dblp:conf/icaart/AnsoWDW19
fatcat:k5i3z262irb6xegeplfbz2ycpa
Deep Illumination: Approximating Dynamic Global Illumination with Generative Adversarial Network
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Inspired from recent advancement in image-to-image translation problems using deep generative convolutional networks, we introduce a variant of this network that learns a mapping from Gbuffers (depth map ...
Our primary contribution is showing that a generative model can be used to learn a density estimation from screen space buffers to an advanced illumination model for a 3D environment. ...
Another technique called Deep Shading [NAM * 17] uses a convolutional neural network to learn a mapping from screen space buffers to various screen space effects, such as ambient occlusion, indirect light ...
arXiv:1710.09834v2
fatcat:qoaxzqus6vewzfaqfenfsyyjym
Classification of Skin Cancer using Deep Learning
2020
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Right now we have arranged the Benign and Malignant skin disease utilizing convolutional neural network. ...
, screening patients with centred skin side effects utilizing physician-directed full body skin assessments. ...
Computers equipped with software based on deep learning, namely convolutional neural networks (CNN) are good at detecting skin cancer than experienced dermatologists. ...
doi:10.5281/zenodo.3749685
fatcat:xdwah7v2vbhghi66dp45djik3q
Deep Light Source Estimation for Mixed Reality
2018
Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
We present a deep learning based technique that estimates point light source position from a single color image. ...
ACKNOWLEDGEMENTS The authors thank Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for the financial support of this work and Nvidia for providing GPUs. ...
Deep Convolution Neural Networks represent the state of the art methodology in several tasks related to visual content analysis, and the topology known as Residual Convolution Neural Network (ResNet) ...
doi:10.5220/0006724303030311
dblp:conf/grapp/MarquesDVC18
fatcat:or5r5dz32rbrncyjgyya72iu3i
Deep residual learning for denoising Monte Carlo renderings
2019
Computational Visual Media
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a deep residual learning based method that outperforms both state-of-the-art handcrafted denoisers and learning-based denoisers. ...
Learning-based techniques have recently been shown to be effective for denoising Monte Carlo rendering methods. However, there remains a quality gap to state-of-the-art handcrafted denoisers. ...
Deep learning for inverse problems Deep convolutional neural networks have demonstrated their great feature extraction power in many difficult image classification problems [30] [31] [32] . ...
doi:10.1007/s41095-019-0142-3
fatcat:wcq3vsk7cnce3ome464bh5izse
Deep Manifold Prior
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We present a prior for manifold structured data, such as surfaces of 3D shapes, where deep neural networks are adopted to reconstruct a target shape using gradient descent starting from a random initialization ...
We show that surfaces generated this way are smooth, with limiting behavior characterized by Gaussian processes, and we mathematically derive such properties for fully-connected as well as convolutional ...
The authors would like to thank Daniel Sheldon for helpful discussions related to Gaussian processes. This work is supported in part by NSF grants #1908669 and #1749833. ...
arXiv:2004.04242v1
fatcat:lfqz7abwyfb7jo3uh2mz6m4sse
Sensing Plant Disease Through the Utility of Deep Learning
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
The series of stimulating global Smart phone penetration including up to date advances also latest traits paved the way for deep Learning knowledge practicing public data sets of infected crops and also ...
healthy plant leaves gathered beneath controlled stipulations, A deep CNN to pick out various crop species including its illnesses(disease) is developed. ...
Convolutional Neural Networks-After disposing of the noise of the photo it is expected to extorts the feature. We will use a CNN for the file image type. ...
doi:10.35940/ijitee.h6463.069820
fatcat:xiruvu62tfa3ffcmijg3dzw724
Deep scattering
2017
ACM Transactions on Graphics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2017 pre-print arXiv:1709.05418v1 fatcat:bv4jtwpgubaczpq5nmuqjyklu4
The descriptor is input to a deep neural network that predicts the radiance function for each shading configuration. ...
We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. ...
ACKNOWLEDGMENTS We thank Delio Vicini for proofreading and helpful discussions, the Stanford 3D Scanning Repository for the Armadillo, Dragon, and StanfordBunny models, and Magnus Wrenninge and the Pixar ...
doi:10.1145/3130800.3130880
fatcat:edmisgifgrb53elcurd7n442bi
« Previous
Showing results 1 — 15 out of 2,286 results