Company Filing History:

11 out of 2,771

Disney Enterprises, Inc.

Eth Zurich (eidgenossische Technische Hochschule Zurich)

patents

4. 1 out of 1

Disnev Enterprises, Inc.

patent

where one patent can have more than one assignee

Years Active: 2019-2024

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12 patents (USPTO):Sort by Forward CitationsExplore Patents

1. 12169914 - Temporal techniques of denoising Monte Carlo renderings using neural networks (2024-12-17),

2. 11532073 - Temporal techniques of denoising Monte Carlo renderings using neural networks (2022-12-20),

3. 11037274 - Denoising Monte Carlo renderings using progressive neural networks (2021-06-15),

4. 10796414 - Kernel-predicting convolutional neural networks for denoising (2020-10-06),

5. 10789686 - Denoising Monte Carlo renderings using machine learning with importance sampling (2020-09-29),

6. 10706508 - Adaptive sampling in Monte Carlo renderings using error-predicting neural networks (2020-07-07),

7. 10699382 - Denoising Monte Carlo renderings using neural networks with asymmetric loss (2020-06-30),

8. 10672109 - Multi-scale architecture of denoising monte carlo renderings using neural networks (2020-06-02),

9. 10607319 - Denoising monte carlo renderings using progressive neural networks (2020-03-31),

10. 10586310 - Denoising Monte Carlo renderings using generative adversarial neural networks (2020-03-10),

11. 10572979 - Denoising Monte Carlo renderings using machine learning with importance sampling (2020-02-25),

12. 10475165 - Kernel-predicting convolutional neural networks for denoising (2019-11-12),

Title: Innovations of Fabrice Rousselle

Introduction

Fabrice Rousselle is a notable inventor based in Ostermundingen, Switzerland. He has made significant contributions to the field of technology, particularly in the area of rendering techniques. With a total of 12 patents to his name, Rousselle's work has garnered attention in the industry.

Latest Patents

One of his latest patents is titled "Temporal techniques of denoising Monte Carlo renderings using neural networks." This innovative modular architecture is designed for denoising Monte Carlo renderings through the use of neural networks. The temporal approach utilized by Rousselle extracts and combines feature representations from neighboring frames, rather than relying on recurrent connections to build a temporal context. His multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, along with one or more scale compositor neural networks that adaptively blend these scales. Additionally, an error-predicting module is configured to produce adaptive sampling maps for a renderer, aiming to achieve a more uniform distribution of residual noise. Rousselle's use of an asymmetric loss function during the training of neural networks allows for better control over the variance-bias trade-off in the denoising process.

Career Highlights

Throughout his career, Rousselle has worked with prominent companies such as Disney Enterprises, Inc. and Pixar. His experience in these leading organizations has undoubtedly influenced his innovative approach to technology and rendering techniques.

Collaborations

Rousselle has collaborated with talented individuals in the industry, including Thijs Vogels and Brian McWilliams. These partnerships have contributed to the development of his groundbreaking inventions.

Conclusion

Fabrice Rousselle is a distinguished inventor whose work in denoising Monte Carlo renderings using neural networks showcases his innovative spirit. His contributions to the field continue to impact the technology landscape significantly.

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