Aakash KT

Selected Publications

1. 

   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] [Code] [Project Page]

   
   

   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.

   
   
2. 

   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] [Project Page] [Genesis]

   
   

   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.

   
   
3. 

   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.

   
   
4. 

   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

   [BibTex] [PDF] [Code] [Project Page] [Genesis]

   
   

   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.

   @article{10.1111:cgf.14895,
     journal = {Computer Graphics Forum},
     title = {{Accelerating Hair Rendering by Learning High-Order Scattered Radiance}},
     author = {KT, Aakash and Jarabo, Adrian and Aliaga, Carlos and Chiang, Matt Jen-Yuan and Maury, Olivier and Hery, Christophe and Narayanan, P. J. and Nam, Giljoo},
     year = {2023},
     publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
     issn = {1467-8659},
     doi = {10.1111/cgf.14895},
     conference_display_name = {Eurographics Symposium on Rendering (EGSR) 2023},
     track_display_name = {Computer Graphics Forum (CGF) Vol. 42, No. 4},
     icon = {/assets/img/egsr2023.png},
     genesis = {/hair_high_order_genesis}
   }

   
   
5. 

   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

   [BibTex] [PDF] [Supp] [Code] [Slides] [Project Page] [Genesis]

   
   

   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.

   @article{10.1145/3522612,
     author = {KT, Aakash and Heitz, Eric and Dupuy, Jonathan and Narayanan, P. J.},
     title = {Bringing Linearly Transformed Cosines to Anisotropic GGX},
     year = {2022},
     issue_date = {May 2022},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     volume = {5},
     number = {1},
     url = {https://doi.org/10.1145/3522612},
     doi = {10.1145/3522612},
     journal = {Proc. ACM Comput. Graph. Interact. Tech.},
     month = may,
     articleno = {12},
     numpages = {18},
     keywords = {area lighting, BRDF, LTC, real-time rendering},
     conference_display_name = {ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D) 2022},
     track_display_name = {PACM-CGIT},
     best_paper = {true},
     icon = {/assets/img/i3d2022.jpg},
     genesis = {/aniso_ltc_genesis}
   }

   
   
6. 

   Fast Analytic Soft Shadows from Area Lights

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

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

   [BibTex] [PDF] [Code] [Slides] [Genesis]

   
   

   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.

   @inproceedings{.20211295,
     booktitle = {Eurographics Symposium on Rendering - DL-only Track},
     editor = {Bousseau, Adrien and McGuire, Morgan},
     title = {{Fast Analytic Soft Shadows from Area Lights}},
     author = {KT, Aakash and Sakurikar, Parikshit and Narayanan, P. J.},
     year = {2021},
     publisher = {The Eurographics Association},
     issn = {1727-3463},
     isbn = {978-3-03868-157-1},
     doi = {10.2312/sr.20211295},
     conference_display_name = {Eurographics Symposium on Rendering (EGSR) 2021},
     track_display_name = {DL-only track},
     icon = {/assets/img/egsr2021.png},
     genesis = {/fast_ss_genesis}
   }

   
   
7. 

   Exploring Data Driven Graphics for Interactivity

   Aakash KT, and P. J. Narayanan

   IIIT-H, Master’s Thesis

   [PDF]

   
   

   
   
8. 

   A Flexible Neural Renderer for Material Visualization

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

   ACM SIGGRAPH Asia 2019, Technical Briefs

   [arXiv] [BibTex] [PDF] [Code] [Project Page] [Genesis]

   
   

   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.

   @inproceedings{KT:2019:FNR:3355088.3365160,
     author = {KT, Aakash and Sakurikar, Parikshit and Saini, Saurabh and Narayanan, P. J.},
     title = {A Flexible Neural Renderer for Material Visualization},
     booktitle = {SIGGRAPH Asia 2019 Technical Briefs},
     series = {SA '19},
     year = {2019},
     isbn = {978-1-4503-6945-9},
     location = {Brisbane, QLD, Australia},
     pages = {83--86},
     numpages = {4},
     url = {http://doi.acm.org/10.1145/3355088.3365160},
     doi = {10.1145/3355088.3365160},
     acmid = {3365160},
     publisher = {ACM},
     address = {New York, NY, USA},
     keywords = {Deep Learning, Global Illumination, Neural Rendering, Ray Tracing},
     conference_display_name = {ACM SIGGRAPH Asia 2019},
     track_display_name = {Technical Briefs},
     icon = {/assets/img/neural-material-vis.png},
     genesis = {/flexible_nr_genesis}
   }