Sayantan
Datta

     

Efficient Graphics Representation with Differentiable Indirection

ACM/SIGGRAPH Asia 2023 Conference Proceedings

Sayantan Datta, Carl Marshall, Derek Nowrouzezahrai, Zhao Dong, Zhengqin Li

Abstract

We introduce differentiable indirection – a novel learned primitive that employs differentiable multi-scale lookup tables as an effective substitute for traditional compute and data operations across the graphics pipeline. We demonstrate its flexibility on a number of graphics tasks, i.e., geometric and image representation, texture mapping, shading, and radiance field representation. In all cases, differentiable indirection seamlessly integrates into existing architectures, trains rapidly, and yields both versatile and efficient results.

[Paper] [Webpage] [Bibtex]

Adaptive Dynamic Global Illumination

High Performance Graphics 2022 Poster

Sayantan Datta, Negar Goli, Jerry Zhang

Abstract

We present an adaptive extension of probe based global illumination solution that enhances the response to dynamic changes in the scene while while also enabling an order of magnitude increase in probe count. Our adaptive sampling strategy carefully places samples in regions where we detect time varying changes in radiosity either due to a change in lighting, geometry or both. Even with large number of probes, our technique robustly updates the irradiance and visibility cache to reflect the most up to date changes without stalling the overall algorithm. Our bandwidth aware approach is largely an improvement over the original DDGI technique while also remaining orthogonal to the recent advancements in the technique.

[Paper] [Webpage] [Bibtex]

Neural Shadow Mapping

ACM/SIGGRAPH 2022 Conference Proceedings

Sayantan Datta, Derek Nowrouzezahrai, Christoph Schied, Zhao Dong

Abstract

We present a neural extension of basic shadow mapping for fast, high quality hard and soft shadows. We compare favorably to fast pre-filtering shadow mapping, all while producing visual results on par with ray traced hard and soft shadows. We show that combining memory bandwidth-aware architecture specialization and careful temporal-window training leads to a fast, compact and easy-to-train neural shadowing method. Our technique is memory bandwidth conscious, eliminates the need for post-process temporal anti-aliasing or denoising, and supports scenes with dynamic view, emitters and geometry while remaining robust to unseen objects.

[Paper] [Supplemental] [Webpage] [Bibtex]

Subspace Neural Physics: Fast Data-Driven Interactive Simulation

ACM/SIGGRAPH Symposium on Computer Animation (SCA)

Daniel Holden, Bang Chi Duong, Sayantan Datta, Derek Nowrouzezahrai

Abstract

Data-driven methods for physical simulation are an attractive option for interactive applications due to their ability to trade precomputation and memory footprint in exchange for improved runtime performance. Yet, existing data-driven methods fall short of the extreme memory and performance constraints imposed by modern interactive applications like AAA games and virtual reality. Here, performance budgets for physics simulation range from tens to hundreds of micro-seconds per frame, per object. We present a data-driven physical simulation method that meets these constraints. Our method combines subspace simulation techniques with machine learning which, when coupled, enables a very efficient subspace-only physics simulation that supports interactions with external objects – a longstanding challenge for existing sub-space techniques. We also present an interpretation of our method as a special case of subspace Verlet integration, where we apply machine learning to efficiently approximate the physical forces of the system directly in the subspace. We propose several practical solutions required to make effective use of such a model, including a novel training methodology required for prediction stability, and a GPU-friendly subspace decompression algorithm to accelerate rendering.

[Paper] [Bibtex]

A Numerical Study of Frictional Contact

Master's thesis, Computer Science, McGill University

Sayantan Datta

Abstract

Friction is a complex phenomenon resulting from elastic and plastic deformations coupled with molecular interaction along the contact boundary. When two surfaces touch, their roughness, and normal force determines the actual area under contact, governing the process of deformation and molecular interaction. A typical macroscopic interaction may involve millions of microscopic contacts and the aggregate of these forces give rise to the phenomenon of friction. In this thesis, our goal is to simulate the phenomenon of friction assuming unlubricated contact and elastic deformation at the contact asperities. We collect data by varying many parameters that affect friction between two surfaces and build a function approximator exploiting the correlation in data. Such an approximator is a computationally inexpensive, versatile and more accurate substitute for friction coefficient tables currently in use with various physically based simulators.

[Thesis] [Bibtex]

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