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Deep learning based 3D reconstruction: supervision and representation / François Darmon (2022)  

Public Titre : Deep learning based 3D reconstruction: supervision and representation Type de document : Thèse/HDR Auteurs : François Darmon, Auteur ; Pascal Monasse, Directeur de thèse ; Mathieu Aubry, Directeur de thèse Editeur : Champs-sur-Marne : Ecole des Ponts ParisTech Année de publication : 2022 Importance : 115 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse de doctorat de l'Ecole des Ponts ParisTech, spécialité informatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] carte de profondeur [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction [Termes IGN] géométrie épipolaire [Termes IGN] maillage [Termes IGN] modèle stéréoscopique [Termes IGN] point d'intérêt [Termes IGN] Ransac (algorithme) [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] semis de points [Termes IGN] SIFT (algorithme) [Termes IGN] structure-from-motion [Termes IGN] voxel Index. décimale : THESE Thèses et HDR Résumé : (auteur) 3D reconstruction is a long standing problem in computer vision. Yet, state-of-the-art methods still struggle when the images used have large illumination changes, many occlusions or limited textures. Deep Learning holds promises of improving 3D reconstruction in such setups, but classical methods still produce the best results. In this thesis we analyse the specificity of deep learning applied to multiview 3D reconstruction and introduce new deep learning based methods.The first contribution of this thesis is an analysis of the possible supervision for training Deep Learning models for sparse image matching. We introduce a two-step algorithm that first computes low resolution matches using deep learning and then matches classical local features inside the matches regions. We analyze several levels of supervision and show that our new epipolar supervision leads to the best results.The second contribution is also a study of supervision for Deep Learning but applied to another scenario: calibrated 3D reconstruction in the wild. We show that existing unsupervised methods do not work on such data and we introduce a new training technique that solves this issue. We then exhaustively compare unsupervised approach and supervised approaches with different network architectures and training data.Finally, our third contribution is about data representation. Neural implicit representation were recently used for image rendering. We adapt this representation to the multiview reconstruction problem and we introduce a new method that, similar to classical 3D reconstruction techniques, optimizes photo-consistency between projections of multiple images. Our approach outperforms state-of-the-art by a large margin. Note de contenu : 1- Introduction 2- Background 3- Deep learning for guiding keypoint matching 4- Deep Learning based Multi-View Stereo in the wild 5- Multi-view reconstruction with implicit surfaces and patch warping 6- Conclusion Numéro de notice : 24085 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Ponts ParisTech : 2022 Organisme de stage : Laboratoire d'Informatique Gaspard-Monge LIGM DOI : sans En ligne : https://www.theses.fr/2022ENPC0024 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102473

The polar epipolar rectification / François Darmon in IPOL Journal, Image Processing On Line, vol 11 (2021)  

Public [article]  inIPOL Journal, Image Processing On Line > vol 11 (2021) . - pp 56 - 75 Titre : The polar epipolar rectification Type de document : Article/Communication Auteurs : François Darmon, Auteur ; Pascal Monasse, Auteur Année de publication : 2021 Article en page(s) : pp 56 - 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] couple stéréoscopique [Termes IGN] disparité [Termes IGN] géométrie épipolaire [Termes IGN] orthorectification [Termes IGN] points homologues Résumé : (auteur) Epipolar rectification of a stereo pair is the process of resampling a pair of stereo images so that the apparent motion of corresponding points is horizontal. This is an important preliminary step in depth estimation, substituting depth by disparity estimation. Most methods rely on a perspective transform of both images, which has the advantage to simulate a different attitude of the pinhole cameras. A limitation is that when an epipole is inside the image domain, it has to be sent to infinity by the perspective transform, producing a strong distortion. On the contrary, relying on a polar transform centered at the epipole provides a method applicable universally to a pair of pinhole camera views. We present in detail the algorithm, filling in the information important for its implementation and missing in published articles. Numéro de notice : A2021-782 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5201/ipol.2021.328 Date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.5201/ipol.2021.328 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98937 [article]