{"id":395,"date":"2016-04-12T22:54:08","date_gmt":"2016-04-12T20:54:08","guid":{"rendered":"http:\/\/members.loria.fr\/GSimon\/?page_id=395"},"modified":"2024-02-06T14:35:56","modified_gmt":"2024-02-06T12:35:56","slug":"cours-3","status":"publish","type":"page","link":"https:\/\/members.loria.fr\/GSimon\/cours-3\/","title":{"rendered":"Cours"},"content":{"rendered":"

R\u00e9alit\u00e9 augment\u00e9e<\/h1>\n\n\n\n
<\/td>\n\n

MASTER 2 Informatique, Universit\u00e9 de Lorraine (12h CM + 12h TP)<\/h2>\n

Diapositives<\/h3>\n

RA_1.pdf<\/a> : Introduction
\nRA_2.pdf<\/a> : G\u00e9om\u00e9trie de la RA
\nRA_3.pdf<\/a> : Calcul de pose
\nRA_4.pdf<\/a> : Odom\u00e9trie visuelle
\nRA_5a.pdf<\/a>, RA_5b.pdf<\/a>, RA_5c.pdf<\/a> : Positionnement global<\/p>\n

Travaux pratiques (Artoolkit)<\/h3>\n

TD ARTOOLKIT.pdf<\/a>
\n
\ntp_artoolkit1.pdf<\/a>
\ntp_artoolkit2.pdf<\/a>
\ntp_artoolkit3.pdf<\/a><\/p>\n

glut.h<\/a> camera_para.dat<\/a> simpleTestTwoMarkers.c<\/a><\/p>\n

Choix d’articles \u00e0 pr\u00e9senter<\/h3>\n

Article 1 : A Novel Representation of Parts for Accurate 3D Object Detection and Tracking<\/em><\/a>
\nArticle 2 : PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization<\/em><\/a>
\nArticle 3 : Real-Time Seamless Single Shot 6D Object Pose Prediction<\/em><\/a>
\nArticle 4 : Implicit 3D Orientation Learning for 6D Object Detection<\/em><\/a>
\nArticle 5 : Place Recognition with ConvNet Landmarks<\/em><\/a>
\nArticle 6 : Facade Proposals for Urban Augmented Reality<\/em><\/a>
\nArticle 7 : OA-SLAM: Leveraging Objects for Camera Relocalization in Visual SLAM<\/em><\/a>
\nArticle 8 : Gaussian Splatting SLAM<\/em><\/a>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Introduction \u00e0 la Vision par Ordinateur<\/h1>\n\n\n\n
<\/td>\n\n

MASTER 1 Informatique, Universit\u00e9 de Lorraine<\/h2>\n

TIVO_part1.pdf<\/a>
\nTIVO_part2.pdf<\/a>
\nTIVO_part3.pdf<\/a>
\nTIVO_tp.pdf<\/a>
\nAthenes.zip<\/a>
\nmatlab.zip<\/a>
\nExamen 2015<\/a>
\nContenu du cours : 1. VISION MONOCULAIRE : la projection centrale (g\u00e9om\u00e9trie), la projection centrale (calculs), calcul du point de vue. 2. VISION BINOCULAIRE : g\u00e9om\u00e9trie de deux cam\u00e9ras, calcul du mouvement, reconstruction 3D, reconstruction dense. 3. VISION MULTIOCULAIRE : suivi d’indices, structure from motion, reconnaissance d’indices, slam visuel, slam dense<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Repr\u00e9sentation des donn\u00e9es visuelles<\/h1>\n\n\n\n
<\/td>\n\n

MASTER 1 Informatique, Universit\u00e9 de Lorraine<\/h2>\n

RDV_CM1_couleur.pdf<\/a>
\nRDV_CM2_maths.pdf<\/a>
\nRDV_CM3_fourier1.pdf<\/a>
\nRDV_CM4_fourier2.pdf<\/a><\/p>\n

RDV_TD1_matlab.pdf<\/a>
\nRDV_TD2_colorim\u00e9trie.pdf<\/a>
\nRDV_TD3_fourier.pdf<\/a><\/p>\n

shadows.jpg<\/a>
\nmonaloa.jpg<\/a>
\nanimal_tres_mechant.jpg<\/a><\/p>\n

 <\/p>\n

 <\/p>\n

 <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Mod\u00e9lisation et int\u00e9gration 3D<\/h1>\n\n\n\n
<\/td>\n\n

LP Infographie paysag\u00e8re, Universit\u00e9 de Lorraine<\/h2>\n

Cours.pdf<\/a><\/p>\n

Contenu du cours : introduction \u00e0 l’imagerie num\u00e9rique – mod\u00e9lisation 3D (introduction \u00e0 Google Sketchup, mod\u00e9lisation \u00e0 partir d’un plan, texturage du mod\u00e8le \u00e0 partir de photographies, mod\u00e9lisation du terrain) – int\u00e9gration 3d (inversion g\u00e9om\u00e9trique d’une photographie, int\u00e9gration 3D et rendu r\u00e9aliste)<\/p>\n

Travaux dirig\u00e9s<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

R\u00e9alit\u00e9 augment\u00e9e<\/p>\n

<\/p>\n

MASTER 2 Informatique, Universit\u00e9 de Lorraine (12h CM + 12h TP)
\nDiapositives<\/p>\n

RA_1.pdf<\/a> : Introduction
\nRA_2.pdf<\/a> : G\u00e9om\u00e9trie de la RA
\nRA_3.pdf<\/a> : Calcul de pose
\nRA_4.pdf<\/a> : Odom\u00e9trie visuelle
\nRA_5a.pdf<\/a>, RA_5b.pdf<\/a>, RA_5c.pdf<\/a> : Positionnement global<\/p>\n

Travaux pratiques (Artoolkit)<\/p>\n

TD ARTOOLKIT.pdf<\/a><\/p>\n

tp_artoolkit1.pdf<\/a>
\ntp_artoolkit2.pdf<\/a>
\ntp_artoolkit3.pdf<\/a><\/p>\n

glut.h<\/a> camera_para.dat<\/a> simpleTestTwoMarkers.c<\/a><\/p>\n

Choix d’articles \u00e0 pr\u00e9senter<\/p>\n

Article 1 : A Novel Representation of Parts for Accurate 3D Object Detection and Tracking<\/em><\/a>
\nArticle 2 : PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization<\/em><\/a>
\nArticle 3 : Real-Time Seamless Single Shot 6D Object Pose Prediction<\/em><\/a>
\nArticle 4 : Implicit 3D Orientation Learning for 6D Object Detection<\/em><\/a>
\nArticle 5 : Place Recognition with ConvNet Landmarks<\/em><\/a>
\nArticle 6 : Facade Proposals for Urban Augmented Reality<\/em><\/a>
\nArticle 7 : OA-SLAM: Leveraging Objects for Camera Relocalization in Visual SLAM<\/em><\/a>
\nArticle 8 : Gaussian Splatting SLAM<\/em><\/a><\/p>\n

Introduction \u00e0 la Vision par Ordinateur<\/p>\n

<\/p>\n

MASTER 1 Informatique,<\/p>\n","protected":false},"author":44,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-fullwidth.php","meta":{"footnotes":""},"class_list":["post-395","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/members.loria.fr\/GSimon\/wp-json\/wp\/v2\/pages\/395","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/members.loria.fr\/GSimon\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/members.loria.fr\/GSimon\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/members.loria.fr\/GSimon\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/members.loria.fr\/GSimon\/wp-json\/wp\/v2\/comments?post=395"}],"version-history":[{"count":96,"href":"https:\/\/members.loria.fr\/GSimon\/wp-json\/wp\/v2\/pages\/395\/revisions"}],"predecessor-version":[{"id":920,"href":"https:\/\/members.loria.fr\/GSimon\/wp-json\/wp\/v2\/pages\/395\/revisions\/920"}],"wp:attachment":[{"href":"https:\/\/members.loria.fr\/GSimon\/wp-json\/wp\/v2\/media?parent=395"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}