Aakash KT

Light Transport | GPU Architecture | Computer Graphics

Hi there! Welcome to my personal page. I love working with practical computer graphics, physics simulations, light transport physics and GPU hardware architectures.

I hold a PhD in real-time rendering from IIIT Hyderabad, supervised by Dr. P. J. Narayanan.

Chronological set of organisations I have been a part of:

Graphics Researcher (ZZP, freelance equivalent), Huawei Ireland Research Center, Dec 2025 - Present
Senior Graphics Research Engineer, Qualcomm, Jun 2024 - Oct 2025
Research Scientist Intern, Meta Reality Labs Pittsburgh, Aug 2022 - Jan 2023

Resume / Email / Google Scholar / Twitter / LinkedIn

Research at a glance

Genshin Impact: Neural Transparency (Huawei)

Complex Appearance

Volume Rendering

Many-light ray-tracing

Hair Rendering

News

Nov 6, 2025	I will soon join Huawei Ireland Research Center (IRC) in Tobias Alexander Franke's graphics research group. I will be working remotely from The Netherlands! But, time for a quick break before that!
Feb 13, 2025	The final work of my PhD has been accepted at Eurographics 2025!
Apr 16, 2024	I will soon join the Graphics Research Team at Qualcomm! Looking forward to coupled research in software and hardware to make real-time physically based rendering a reality.
Apr 1, 2024	I am taking a few guest lectures for the Computer Graphics course at IIT Jodhpur. These lectures will be on physically based rendering with ray tracing - following our course at IIIT Hyderabad. Many thanks to Prof. Avinash for the invitation!
Jan 2, 2024	I am teaching Introduction to Computer Graphics course at IIIT Hyderabad with Prof. PJN. We have revamped this course to teach Physically Based Rendering, following the excellent reference PBRT-v3 and Prof. Wojciech Jarosz's rendering course at Darthmouth.
Sep 26, 2023	Our paper on combining RIS with projected solid angle sampling was accepted at SIGGRAPH Asia Technical Communications!
Sep 6, 2023	I will present a summary of my PhD thesis at the Doctoral Consortium of SIGGRAPH Asia 2023 in Sydney, Australia.
May 26, 2023	Our work during my internship at Meta Reality Labs, Pittsburgh has been accepted to EGSR 2023, and will also be published in CGF Journal!
May 5, 2022	We received the best paper award at I3D for our paper "Bringing Linearly Transformed Cosines to Anisotropic GGX"! NVIDIA was generous to gift us a RTX 3090 for this. Thank you!
Mar 29, 2022	I will be joining Meta Reality Labs, Pittsburgh (formerly Facebook Reality Labs) as a Research Scientist Intern in August. I will be working in Yaser Sheikh's team, with Giljoo Nam. Looking forward to some exciting work on Photorealistic Telepresence!
Mar 10, 2022	Our paper "Bringing Linearly Transformed Cosines to Anisotropic GGX" has been accepted to I3D 2022! This work was in collaboration with Eric Heitz and Jonathan Dupuy from Unity Research Grenoble.
Mar 4, 2022	Our work "Precomputed Radiance Transfer Using Transfer Textures" has been accepted as a poster to EG 2022.
Jun 5, 2021	Our paper "Fast Analytic Soft Shadows from Area Lights" has been accepted to EGSR 2021!
Aug 1, 2020	I have joined as a Ph.D student at CVIT, IIIT Hyderabad, supervised by Dr. P. J. Narayanan. My Ph.D is funded by the KCIS fellowship.
Feb 1, 2020	I was to join the GraphDeco research group @ Inria (Sophia Antipolis, France), as a pre-doctoral candidate from September 2020, supervised by Dr. George Drettakis and Dr. Adrien Bousseau. This was unfortunately rescinded due to COVID.

Selected Publications

Linearly Transformed Spherical Distributions for Interactive Single Scattering with Area Lights

Aakash KT, Ishaan Shah, and P. J. Narayanan

Eurographics 2025, Computer Graphics Forum (CGF)

[PDF] [Supp]

Linearly Transformed Spherical Distributions (LTSDs), a superset of the commonly known Linearly Transformed Cosines (LTCs) for analytic area light rendering, applied in the context of analytic area light rendering with participating media.

Combining Resampled Importance and Projected Solid Angle Samplings for Many Area Light Rendering

Ishaan Shah*, Aakash KT*, and P. J. Narayanan

ACM SIGGRAPH Asia 2023, Technical Communications (Formerly Technical Briefs)

[PDF] [Code]

We identify the core issue for the high run-times of a naive combination of RIS and projected solid angle sampling. We then reformulate RIS for a better integration with projected solid angle sampling, achieving the state-of-the-art in many area light rendering.

Analytical & Neural approaches to Physically Based Rendering

Aakash KT

ACM SIGGRAPH Asia 2023, Doctoral Consortium

[PDF]

Summary of my PhD thesis, including two extensions to LTCs and the extension of NRC for efficient hair rendering.

Accelerating Hair Rendering by Learning High-Order Scattered Radiance

Aakash KT, Adrian Jarabo, Carlos Aliaga, Matt Jen-Yuan Chiang, Olivier Maury, Christophe Hery, P. J. Narayanan, and Giljoo Nam

Eurographics Symposium on Rendering (EGSR) 2023, Computer Graphics Forum (CGF) Vol. 42, No. 4

[PDF] [Code]

Efficiently and accurately rendering hair accounting for multiple scattering is a challenging open problem. We present a technique to infer the higher order scattering in hair in constant time within the path tracing framework, while achieving better computational efficiency.

Bringing Linearly Transformed Cosines to Anisotropic GGX

Aakash KT, Eric Heitz, Jonathan Dupuy, and P. J. Narayanan

ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D) 2022, PACM-CGIT, Best Paper Award

[PDF] [Supp] [Code]

We present an extension to LTCs for anisotropic GGX, in the context of real-time analytic area light shading. Our approach robustly fits LTC matrices to anisotropic GGX and ensures artefact free rendering. The end result is a 8⁴ LUT parameterized by the elevation and azimuth of view vector and the roughness in x and y directions.

Fast Analytic Soft Shadows from Area Lights

Aakash KT, Parikshit Sakurikar, and P. J. Narayanan

Eurographics Symposium on Rendering (EGSR) 2021, DL-only track

[PDF] [Code]

We present an analytical solution for soft shadows from area lights, which naturally produces noise-free renderings as compared to equivalent stochastic methods. A structured approach to analytically compute soft shadows from spherical projections of lights and occluders with any 3D shape and efficiently for convex 3D shapes.

A Flexible Neural Renderer for Material Visualization

Aakash KT, Parikshit Sakurikar, Saurabh Saini, and P. J. Narayanan

ACM SIGGRAPH Asia 2019, Technical Briefs

[PDF] [arXiv] [Code]

Photo realism in computer generated imagery is crucially dependent on how well an artist is able to recreate real-world materials in the scene. We propose a convolutional neural network based workflow which generates high-quality ray traced material visualizations on a shaderball, in real-time.

Exploring Data Driven Graphics for Interactivity

Aakash KT and P. J. Narayanan

IIIT-H, Master's Thesis

[PDF]