San Francisco, CA, United States of America

Noam Aigerman

USPTO Granted Patents = 13 (Patent Applications = 4)

Average Co-Inventor Count = 3.0

ph-index = 1

Forward Citations = 3(Granted Patents)

Company Filing History:

where one patent can have more than one assignee

Years Active: 2021-2025

where 'Filed Patents' based on already Granted Patents

13 patents (USPTO):Sort by Forward Citations

Abstract keywords

1. 12374043 - Utilizing machine learning models for patch retrieval and deformation in completing three-dimensional digital shapes (2025-07-29),

2. 12347034 - Generating mappings of three-dimensional meshes utilizing neural network gradient fields (2025-07-01),

3. 12118669 - Subdividing a three-dimensional mesh utilizing a neural network (2024-10-15),

4. 11869132 - Neural network based 3D object surface mapping (2024-01-09),

5. 11810255 - Swept volume determination techniques (2023-11-07),

6. 11727636 - Three-dimensional mesh segmentation (2023-08-15),

7. 11694416 - Intuitive editing of three-dimensional models (2023-07-04),

8. 11423617 - Subdividing a three-dimensional mesh utilizing a neural network (2022-08-23),

9. 11403807 - Learning hybrid (surface-based and volume-based) shape representation (2022-08-02),

10. 11257290 - Decimating a three-dimensional mesh via successive self-parameterization (2022-02-22),

11. 11127205 - Three-dimensional mesh segmentation (2021-09-21),

12. 11080819 - Generating developable depth images using rank minimization (2021-08-03),

13. 10957117 - Intuitive editing of three-dimensional models (2021-03-23),

Title: Innovations of Noam Aigerman

Introduction

Noam Aigerman is a prominent inventor based in San Francisco, CA, known for his significant contributions to the field of machine learning and 3D digital shape processing. With a total of 13 patents to his name, Aigerman has made remarkable strides in utilizing advanced technologies to enhance digital modeling and representation.

Latest Patents

Aigerman's latest patents include groundbreaking methods for utilizing machine learning models for patch retrieval and deformation in completing three-dimensional digital shapes. These systems predict a coarse completion shape from an incomplete 3D digital shape, sampling coarse 3D patches and learning a shape distance function to retrieve detailed 3D shape patches. Additionally, he has developed systems for generating mappings of three-dimensional meshes utilizing neural network gradient fields. This innovation involves generating digital chain pull paintings in digital images by determining a gradient field based on matrices of polygons associated with the three-dimensional mesh.

Career Highlights

Throughout his career, Aigerman has worked with notable companies such as Adobe, Inc. and UCL Business Plc. His experience in these organizations has allowed him to refine his skills and contribute to cutting-edge projects in the tech industry.

Collaborations

Aigerman has collaborated with talented individuals in his field, including Vladimir Kim and Siddhartha Chaudhuri. These partnerships have fostered innovation and creativity in his work.

Conclusion

Noam Aigerman's contributions to the field of machine learning and 3D modeling are noteworthy. His innovative patents and collaborations highlight his role as a leading inventor in the technology sector.

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4 patents pending:

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