Cambridge, MA, United States of America

Kalyan Krishna Sunkavalli

This inventor holds 93 USPTO granted patents and 14 published patent applications, primarily in Neural Image Processing (CPC class G06T2207-20081). Top assignees: Adobe, Inc., Universitè Laval, Microsoft Technology Licensing, LLC. Active years: 2010-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Kalyan Krishna Sunkavalli. Retrieved from https://idiyas.com/inventor/kalyan-krishna-sunkavalli

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 93 

% Patents Active = 98.9

Average Co-Inventor Count = 4.2

ph-index = 10

Forward Citations = 283(Granted Patents)

Forward Citations (Not Self Cited) = 209(Dec 10, 2025)


Inventors with similar research interests:

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Location History:

  • Cambridge, MA (US) (2010 - 2014)
  • San Jose, CA (US) (2016 - 2024)

Company Filing History:


Years Active: 2010-2026

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Areas of Expertise:
Radiance Field
3D Models
Neural Networks
Image Harmonization
Digital Images
Material Generation
Scene Reconstruction
Texture Mapping
Lighting Estimation
Video Segmentation
Augmented Reality
Image Modification
93 patents (USPTO):Explore Patents

Title: Innovations by Kalyan Krishna Sunkavalli: Pioneering Scene Reconstruction Technologies

Introduction: Kalyan Krishna Sunkavalli, based in Cambridge, MA, is an accomplished inventor with an impressive portfolio of 84 patents. His work primarily focuses on advanced technologies in scene reconstruction, showcasing his innovative contributions to the field.

Latest Patents: Among his latest patents, Kalyan has developed a groundbreaking system for scene reconstruction using geometry and reflectance volume representation. This system enhances image rendering of a scene with high-quality geometry and appearance while facilitating view synthesis, relighting, and scene editing. By training a network on input images, it learns a volume representation encompassing distinct geometry and reflectance parameters. Consequently, the system can render images of a scene from arbitrary viewing and lighting locations, as well as modify the reflectance of objects within the scene to achieve desired edits.

Another significant patent involves editing neural radiance fields through neural basis decomposition. This method provides systems and techniques for generating editable synthesized views of scenes by utilizing image rays as inputs into neural networks. It employs a comprehensive approach where a collection of input images generates multiple rays corresponding to three-dimensional coordinates and viewing angles. A first neural network is tasked with determining the volume density of the scene, while a second neural network produces an appearance decomposition, resulting in a detailed 3D geometric representation of objects within the scene.

Career Highlights: Kalyan has worked with renowned companies such as Adobe Inc. and Adobe Systems Inc., where he has made substantial contributions to the field of computer graphics and image processing. His work has not only advanced technological capabilities but has also enriched the industry with state-of-the-art methodologies.

Collaborations: Throughout his career, Kalyan has collaborated with esteemed colleagues like Sunil Sharadchandra Hadap and Elya Shechtman. These collaborations have further fueled innovation and excellence in his projects, enabling the development of cutting-edge technologies within the realm of scene reconstruction.

Conclusion: Kalyan Krishna Sunkavalli's contributions to the field of scene reconstruction demonstrate the impact of innovative thinking and collaboration. With a substantial number of patents and transformative technologies, he continues to lead advancements in the industry, inspiring a new generation of inventors and innovators in the realm of computer graphics and image processing.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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