Search
Publication Authors

Prof. Dr. Didier Stricker

Dr. Alain Pagani

Dr. Gerd Reis

Eric Thil

Keonna Cunningham

Dr. Oliver Wasenmüller

Dr. Muhammad Zeshan Afzal

Dr. Gabriele Bleser

Dr. Muhammad Jameel Nawaz Malik
Dr. Bruno Mirbach

Dr. Jason Raphael Rambach

Dr. Nadia Robertini

Dr. René Schuster

Dr. Bertram Taetz

Ahmed Aboukhadra

Sk Aziz Ali

Mhd Rashed Al Koutayni

Yuriy Anisimov

Jilliam Maria Diaz Barros

Ramy Battrawy
Hammad Butt

Mahdi Chamseddine

Steve Dias da Cruz
Fangwen Shu

Torben Fetzer

Ahmet Firintepe
Sophie Folawiyo

David Michael Fürst

Christiano Couto Gava

Tewodros Amberbir Habtegebrial
Simon Häring

Khurram Azeem Hashmi
Henri Hoyez

Jigyasa Singh Katrolia

Andreas Kölsch
Onorina Kovalenko

Stephan Krauß
Paul Lesur

Michael Lorenz

Dr. Markus Miezal

Mina Ameli

Nareg Minaskan Karabid

Mohammad Minouei

Pramod Murthy

Mathias Musahl

Peter Neigel

Manthan Pancholi
Mariia Podguzova

Praveen Nathan
Qinzhuan Qian
Rishav
Marcel Rogge
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar

Alexander Schäfer

Pascal Schneider

Mohamed Selim

Tahira Shehzadi
Lukas Stefan Staecker

Yongzhi Su

Xiaoying Tan
Christian Witte

Yaxu Xie

Vemburaj Yadav

Dr. Vladislav Golyanik

Dr. Aditya Tewari

André Luiz Brandão
Publication Archive
New title
- ActivityPlus
- AlterEgo
- AR-Handbook
- ARVIDA
- Auroras
- AVILUSplus
- Be-greifen
- Body Analyzer
- CAPTURE
- COGNITO
- DAKARA
- Density
- DYNAMICS
- EASY-IMP
- Eyes Of Things
- iACT
- IMCVO
- IVMT
- LARA
- LiSA
- Marmorbild
- Micro-Dress
- Odysseus Studio
- On Eye
- OrcaM
- PAMAP
- PROWILAN
- ServiceFactory
- STREET3D
- SUDPLAN
- SwarmTrack
- TuBUs-Pro
- VIDETE
- VIDP
- VisIMon
- VISTRA
- You in 3D
Robust and Accurate Non-Parametric Estimation of Reflectance using Basis Decomposition and Correction Functions
Robust and Accurate Non-Parametric Estimation of Reflectance using Basis Decomposition and Correction Functions
Tobias Nöll, Johannes Köhler, Didier Stricker
Proceedings of the European Conference on Computer Vision (ECCV) European Conference on Computer Vision (ECCV-14), September 6-12, Zürich, Switzerland
- Abstract:
- A common approach to non-parametric BRDF estimation is the approximation of the sparsely measured input using basis decomposition. In this paper we greatly improve the fitting accuracy of such methods by iteratively applying a novel correction function to an initial estimate. We also introduce a basis to efficiently represent such a function. Based on this general concept we propose an iterative algorithm that is able to explicitly identify and treat outliers in the input data. Our method is invariant to different error metrics which alleviates the error-prone choice of an appropriate one for the given input. We evaluate our method based on a large set of experiments generated from 100 real-world BRDFs and 16 newly measured materials. The experiments show that our method outperforms other evaluated state-of-the-art basis decomposition methods by an order of magnitude in the perceptual sense for outlier ratios up to 40%.
- Keywords:
- Non-parametric BRDF estimation, reflectance, basis decompostion, correction function, error metric, sparse data, outliers