View source for 18083354. METHOD OF GENERATING MULTI-LAYER REPRESENTATION OF SCENE AND COMPUTING DEVICE IMPLEMENTING THE SAME simplified abstract (Samsung Electronics Co., Ltd.)

=METHOD OF GENERATING MULTI-LAYER REPRESENTATION OF SCENE AND COMPUTING DEVICE IMPLEMENTING THE SAME=


==Organization Name==

[[:Category:Samsung Electronics Co., Ltd.|Samsung Electronics Co., Ltd.]]


[[Category:Samsung Electronics Co., Ltd.]]

==Inventor(s)==

[[:Category:Gleb Sterkin of Moscow (RU)|Gleb Sterkin of Moscow (RU)]][[Category:Gleb Sterkin of Moscow (RU)]]

[[:Category:Pavel Ilyich Solovev of Moscow (RU)|Pavel Ilyich Solovev of Moscow (RU)]][[Category:Pavel Ilyich Solovev of Moscow (RU)]]

[[:Category:Denis Mikhaylovich Korzhenkov of Moscow (RU)|Denis Mikhaylovich Korzhenkov of Moscow (RU)]][[Category:Denis Mikhaylovich Korzhenkov of Moscow (RU)]]

[[:Category:Victor Sergeevich Lempitsky of Yerevan (RU)|Victor Sergeevich Lempitsky of Yerevan (RU)]][[Category:Victor Sergeevich Lempitsky of Yerevan (RU)]]

[[:Category:Taras Andreevich Khakhulin of Moscow (RU)|Taras Andreevich Khakhulin of Moscow (RU)]][[Category:Taras Andreevich Khakhulin of Moscow (RU)]]

==METHOD OF GENERATING MULTI-LAYER REPRESENTATION OF SCENE AND COMPUTING DEVICE IMPLEMENTING THE SAME - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18083354 titled 'METHOD OF GENERATING MULTI-LAYER REPRESENTATION OF SCENE AND COMPUTING DEVICE IMPLEMENTING THE SAME

==Simplified Explanation==

The present disclosure is about a method of generating a multi-layer representation of a scene using neural networks trained in an end-to-end fashion. The method involves obtaining a pair of images of the scene, performing a reprojection operation on the images to generate a plane-sweep volume, predicting a layered structure of the scene using a geometry network, and estimating color values and opacity values for the predicted layered structure using a coloring network.

* The method uses neural networks to generate a multi-layer representation of a scene.
* It involves obtaining a pair of images, a reference image, and a source image.
* A reprojection operation is performed on the images to create a plane-sweep volume.
* A geometry network predicts the layered structure of the scene based on the plane-sweep volume.
* A coloring network estimates color values and opacity values for the predicted layered structure.
* The geometry network and the coloring network are trained in an end-to-end manner.

==Potential Applications==
* This technology can be used in computer graphics and virtual reality applications to generate realistic multi-layer representations of scenes.
* It can be applied in video games to create more immersive and visually appealing environments.
* It can be used in film and animation production to generate complex and detailed scenes.

==Problems Solved==
* Traditional methods of generating multi-layer representations of scenes may be time-consuming and require manual intervention.
* This technology solves the problem of accurately representing the layered structure of a scene using neural networks.
* It automates the process of generating multi-layer representations, saving time and effort.

==Benefits==
* The method provides a more efficient and automated way of generating multi-layer representations of scenes.
* It produces realistic and detailed representations, enhancing the visual quality of computer graphics and virtual reality applications.
* The end-to-end training of the neural networks ensures a seamless integration of the geometry and coloring processes.


==Original Abstract Submitted==

The present disclosure relates to the field of artificial intelligence (AI) and neural rendering, and particularly to a method of generating a multi-layer representation of a scene using neural networks trained in an end-to-end fashion and to a computing device implementing the method. The method of generating a multi-layer representation of a scene includes: obtaining a pair of images of the scene, the pair of the images comprising a reference image and a source image; performing a reprojection operation on the pair of images to generate a plane-sweep volume; predicting, using a geometry network, a layered structure of the scene based on the plane-sweep volume; and estimating, using a coloring network, color values and opacity values for the predicted layered structure of the scene to obtain the multi-layer representation of the scene; wherein the geometry network and the coloring network are trained in end-to-end manner.



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