Publications by Sébastien Nicolas Speierer

Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering

Sébastien Nicolas Speierer, Nicolas Julien Roussel, Wenzel Alban Jakob, Delio Aleardo Vicini

Dr.Jit is a new just-in-time compiler for physically based rendering and its derivative. Dr.Jit expedites research on these topics in two ways: first, it traces high-level simulation code (e.g., written in Python) and aggressively simplifies and specialize ...

2022

Differentiable Signed Distance Function Rendering

Sébastien Nicolas Speierer, Wenzel Alban Jakob, Delio Aleardo Vicini

Physically-based differentiable rendering has recently emerged as an attractive new technique for solving inverse problems that recover complete 3D scene representations from images. The inversion of shape parameters is of particular interest but also pose ...

ASSOC COMPUTING MACHINERY2022

Path Replay Backpropagation: Differentiating Light Paths using Constant Memory and Linear Time

Sébastien Nicolas Speierer, Wenzel Alban Jakob, Delio Aleardo Vicini

Differentiable physically-based rendering has become an indispensable tool for solving inverse problems involving light. Most applications in this area jointly optimize a large set of scene parameters to minimize an objective function, in which case revers ...

ASSOC COMPUTING MACHINERY2021

Monte Carlo Estimators for Differential Light Transport

Sébastien Nicolas Speierer, Wenzel Alban Jakob, Tizian Lucien Zeltner

Physically based differentiable rendering algorithms propagate derivatives through realistic light transport simulations and have applications in diverse areas including inverse reconstruction and machine learning. Recent progress has led to unbiased metho ...

ASSOC COMPUTING MACHINERY2021

Radiative Backpropagation: An Adjoint Method for Lightning-Fast Differentiable Rendering

Sébastien Nicolas Speierer, Merlin Eléazar Nimier-David, Wenzel Alban Jakob

Physically based differentiable rendering has recently evolved into a powerful tool for solving inverse problems involving light. Methods in this area perform a differentiable simulation of the physical process of light transport and scattering to estimate ...

ASSOC COMPUTING MACHINERY2020

High Fidelity Visualization of Large Scale Digitally Reconstructed Brain Circuitry with Signed Distance Functions

Felix Schürmann, Marwan Muhammad Ahmed Abdellah, Sébastien Nicolas Speierer, Samuel Lieven D. Lapere, Jonas Kristofer Karlsson, Alessandro Enrico Foni

We explore a first proof-of-concept application for visualizing large scale digitally reconstructed brain circuitry using signed distance functions. The significance of our method is demonstrated in comparison with using implicit geometry that is limited t ...

IEEE2019