Fast and accurate surface normal integration on non-rectangular domains

Autor: Ali Sharifi Boroujerdi, Yvain Quéau, Martin Bähr, Michael Breuß, Jean-Denis Durou
Přispěvatelé: Brandenburg University of Technology [Cottbus – Senftenberg] (BTU), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Real Expression Artificial Life (IRIT-REVA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Brandenburgische Technische Universität Cottbus-Senftenberg - BTU (GERMANY), Institut de Recherche en Informatique de Toulouse - IRIT (Toulouse, France), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Jazyk: angličtina
Rok vydání: 2017
Předmět:
FOS: Computer and information sciences
Mathematical optimization
Photometric stereo
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Fast marching method
68U10
Preconditioning
02 engineering and technology
Conjugate gradient method
lcsh:QA75.5-76.95
Computational science
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Poisson integration
Traitement des images
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Artificial Intelligence
Robustness (computer science)
preconditioning
FOS: Mathematics
0202 electrical engineering
electronic engineering
information engineering
Conjugate residual method
Traitement du signal et de l'image
3D reconstruction
Synthèse d'image et réalité virtuelle
fast marching method
Mathematics
Computer Science - Numerical Analysis
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
020207 software engineering
Numerical Analysis (math.NA)
Krylov subspace
Vision par ordinateur et reconnaissance de formes
Solver
Intelligence artificielle
Computer Graphics and Computer-Aided Design
[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]
Surface normal integration
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
conjugate gradient method
Krylov subspace methods
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
lcsh:Electronic computers. Computer science
surface normal integration
Zdroj: Computational Visual Media, Vol 3, Iss 2, Pp 107-129 (2017)
Computational Visual Media
Computational Visual Media, Springer, 2017, vol. 3 (n° 2), pp. 107-129. ⟨10.1007/s41095-016-0075-z⟩
ISSN: 2096-0662
2096-0433
DOI: 10.1007/s41095-016-0075-z
Popis: The integration of surface normals for the purpose of computing the shape of a surface in 3D space is a classic problem in computer vision. However, even nowadays it is still a challenging task to devise a method that combines the flexibility to work on non-trivial computational domains with high accuracy, robustness and computational efficiency. By uniting a classic approach for surface normal integration with modern computational techniques we construct a solver that fulfils these requirements. Building upon the Poisson integration model we propose to use an iterative Krylov subspace solver as a core step in tackling the task. While such a method can be very efficient, it may only show its full potential when combined with a suitable numerical preconditioning and a problem-specific initialisation. We perform a thorough numerical study in order to identify an appropriate preconditioner for our purpose. To address the issue of a suitable initialisation we propose to compute this initial state via a recently developed fast marching integrator. Detailed numerical experiments illuminate the benefits of this novel combination. In addition, we show on real-world photometric stereo datasets that the developed numerical framework is flexible enough to tackle modern computer vision applications.
Databáze: OpenAIRE
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