Aakash KT

Selected Publications

1. 

   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.

   
   
2. 

   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.

   
   
3. 

   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}
   }

   
   
4. 

   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}
   }

   
   
5. 

   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}
   }

   
   
6. 

   Exploring Data Driven Graphics for Interactivity

   Aakash KT, and P. J. Narayanan

   IIIT-H, Master’s Thesis

   [PDF]

   
   

   
   
7. 

   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}
   }