Learning to Learn and Sample BRDFs

7 October 2022

Papers citing "Learning to Learn and Sample BRDFs"

28 / 28 papers shown

Title
Metappearance: Meta-Learning for Visual Appearance Reproduction Michael Fischer Tobias Ritschel 3DH 42 10 0 19 Apr 2022
Active Exploration for Neural Global Illumination of Variable Scenes Stavros Diolatzis Julien Philip G. Drettakis 3DV BDL 29 22 0 15 Mar 2022
Neural BRDFs: Representation and Operations Jiahui Fan Beibei Wang Miloš Hašan Jian Yang Ling-Qi Yan AI4CE 46 6 0 06 Nov 2021
Fast Training of Neural Lumigraph Representations using Meta Learning Alexander W. Bergman Petr Kellnhofer Gordon Wetzstein 86 40 0 28 Jun 2021
Real-time Neural Radiance Caching for Path Tracing Thomas Müller Fabrice Rousselle Jan Novák A. Keller 3DH AI4CE 65 161 0 23 Jun 2021
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images Shaofei Wang Marko Mihajlovic Qianli Ma Andreas Geiger Siyu Tang 3DH 53 100 0 22 Jun 2021
Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering Vincent Sitzmann Semon Rezchikov William T. Freeman J. Tenenbaum F. Durand 3DV 159 294 0 04 Jun 2021
Generative Modelling of BRDF Textures from Flash Images Philipp Henzler Valentin Deschaintre Niloy J. Mitra Tobias Ritschel 3DV 50 66 0 23 Feb 2021
Neural BRDF Representation and Importance Sampling Alejandro Sztrajman G. Rainer Tobias Ritschel Tim Weyrich 36 59 0 11 Feb 2021
Learned Initializations for Optimizing Coordinate-Based Neural Representations Matthew Tancik B. Mildenhall Terrance Wang Divi Schmidt Pratul P. Srinivasan Jonathan T. Barron Ren Ng 99 290 0 03 Dec 2020
learn2learn: A Library for Meta-Learning Research Sébastien M. R. Arnold Praateek Mahajan Debajyoti Datta Ian Bunner Konstantinos Saitas Zarkias 80 95 0 27 Aug 2020
Guided Fine-Tuning for Large-Scale Material Transfer Valentin Deschaintre G. Drettakis Adrien Bousseau DiffM 3DV 65 52 0 06 Jul 2020
MetaSDF: Meta-learning Signed Distance Functions Vincent Sitzmann E. R. Chan Richard Tucker Noah Snavely Gordon Wetzstein 62 247 0 17 Jun 2020
Seeing the World in a Bag of Chips Jeong Joon Park Aleksander Holynski S. M. Seitz 3DV 50 35 0 14 Jan 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 379 42,299 0 03 Dec 2019
Single-Image SVBRDF Capture with a Rendering-Aware Deep Network Valentin Deschaintre M. Aittala F. Durand G. Drettakis Adrien Bousseau 3DH 44 266 0 23 Oct 2018
Deep Appearance Maps Maxim Maximov Laura Leal-Taixé Mario Fritz Tobias Ritschel 3DH FAtt 3DV 44 33 0 03 Apr 2018
Not All Samples Are Created Equal: Deep Learning with Importance Sampling Angelos Katharopoulos François Fleuret 84 517 0 02 Mar 2018
Safe Adaptive Importance Sampling Sebastian U. Stich Anant Raj Martin Jaggi 57 54 0 07 Nov 2017
Material Editing Using a Physically Based Rendering Network Guilin Liu Duygu Ceylan Ersin Yumer Jimei Yang Jyh-Ming Lien DiffM 47 94 0 01 Aug 2017
Meta-SGD: Learning to Learn Quickly for Few-Shot Learning Zhenguo Li Fengwei Zhou Fei Chen Hang Li 92 1,116 0 31 Jul 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 806 11,866 0 09 Mar 2017
SGDR: Stochastic Gradient Descent with Warm Restarts I. Loshchilov Frank Hutter ODL 288 8,091 0 13 Aug 2016
Deep Reflectance Maps Konstantinos Rematas Tobias Ritschel Mario Fritz E. Gavves Tinne Tuytelaars 62 103 0 13 Nov 2015
Online Learning to Sample Guillaume Bouchard Théo Trouillon J. Perez Adrien Gaidon OffRL OnRL 57 30 0 30 Jun 2015
Variational Inference with Normalizing Flows Danilo Jimenez Rezende S. Mohamed DRL BDL 296 4,167 0 21 May 2015
Stochastic Optimization with Importance Sampling P. Zhao Tong Zhang 88 344 0 13 Jan 2014
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm Deanna Needell Nathan Srebro Rachel A. Ward 134 551 0 21 Oct 2013