Název:	A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Dataset
Autoři:	Hardy, Clément Quéau, Yvain Tschumperlé, David
Citace zdrojového dokumentu:	WSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 194-203.
Datum vydání:	2023
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/54425
ISBN:	978-80-86943-32-9
ISSN:	2464–4617 (print) 2464–4625 (CD/DVD)
Klíčová slova:	fotometrické stereo;3D rekonstrukce;běžný mapový odhad;víceúrovňová architektura;nový datový soubor
Klíčová slova v dalším jazyce:	photometric stereo;3D-recontruction;normal map estimation;multi-scale achitecture;new dataset
Abstrakt v dalším jazyce:	The photometric stereo (PS) problem consists in reconstructing the 3D-surface of an object, thanks to a set of photographs taken under different lighting directions. In this paper, we propose a multi-scale architecture for PS which, combined with a new dataset, yields state-of-the-art results. Our proposed architecture is flexible: it permits to consider a variable number of images as well as variable image size without loss of performance. In addition, we define a set of constraints to allow the generation of a relevant synthetic dataset to train convolutional neural networks for the PS problem. Our proposed dataset is much larger than pre-existing ones, and contains many objects with challenging materials having anisotropic reflectance (e.g. metals, glass). We show on publicly available benchmarks that the combination of both these contributions drastically improves the accuracy of the estimated normal field, in comparison with previous state-of-the-art methods.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2023: Full Papers Proceedings

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