[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-967505","data":"\u003Cdiv class=\u0022container content-rte\u0022\u003E\n  \u003Cdiv class=\u0022row\u0022\u003E\n    \u003Cdiv class=\u0022box col-md-6 bg-primary text-white\u0022\u003E\n      \u003Ch2 class=\u0022h1 text-left\u0022\u003E  3D data analysis\n\u003C\/h2\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n          3\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ESemester:\n          Semestre 1\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n            \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ED\u00e9tail du volume horaire\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                          \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003ELecture:\u003C\/span\u003E\n                                      \u003Cspan\u003E12\u003C\/span\u003E\n                      \u003C\/div\u003E\n                                \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                          \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003EPractical study :\u003C\/span\u003E\n                                      \u003Cspan\u003E8\u003C\/span\u003E\n                      \u003C\/div\u003E\n                                \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                          \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003EDirected study:\u003C\/span\u003E\n                                      \u003Cspan\u003E6\u003C\/span\u003E\n                      \u003C\/div\u003E\n                                                            \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ELangue d\u0026#039;enseignement\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EAnglais\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EEnseignement \u00e0 distance\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003Enon\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n    \u003Cdiv class=\u0022box col-md-6\u0022\u003E\n                    \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EProgramme \/ plan \/ contenus\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E1. Introduction to 3D Data\u003C\/p\u003E\n\u003Cp\u003ENature and sources of 3D data: depth sensors, LiDAR, stereo vision, multi-view reconstruction\u003C\/p\u003E\n\u003Cp\u003E3D data representations: point clouds, meshes, voxel grids, implicit functions\u003C\/p\u003E\n\u003Cp\u003ECoordinate systems and geometric transformations\u003C\/p\u003E\n\u003Cp\u003E2. 3D Data Acquisition \u0026amp; Preprocessing\u003C\/p\u003E\n\u003Cp\u003ECamera models and calibration\u003C\/p\u003E\n\u003Cp\u003EDepth estimation and stereo reconstruction\u003C\/p\u003E\n\u003Cp\u003EPoint cloud filtering, registration, and alignment (ICP, RANSAC)\u003C\/p\u003E\n\u003Cp\u003ENormal estimation and surface reconstruction\u003C\/p\u003E\n\u003Cp\u003E3. 3D Feature Extraction \u0026amp; Classical Methods\u003C\/p\u003E\n\u003Cp\u003EGeometric descriptors: curvature, normals, shape histograms (FPFH, SHOT)\u003C\/p\u003E\n\u003Cp\u003EKeypoint detection in 3D\u003C\/p\u003E\n\u003Cp\u003EObject segmentation and classification with traditional approaches\u003C\/p\u003E\n\u003Cp\u003E4. Deep Learning for 3D Data\u003C\/p\u003E\n\u003Cp\u003E3D convolutional networks (3D CNNs, volumetric approaches)\u003C\/p\u003E\n\u003Cp\u003EPoint-based architectures: PointNet, PointNet++, DGCNN\u003C\/p\u003E\n\u003Cp\u003EGraph-based methods for 3D learning\u003C\/p\u003E\n\u003Cp\u003EMesh and implicit representation learning\u003C\/p\u003E\n\u003Cp\u003E5. Multi-View and Hybrid Approaches\u003C\/p\u003E\n\u003Cp\u003EMulti-view CNNs and differentiable rendering\u003C\/p\u003E\n\u003Cp\u003ENeural Radiance Fields (NeRF) and implicit scene representation\u003C\/p\u003E\n\u003Cp\u003E2D\u20133D joint learning and fusion techniques\u003C\/p\u003E\n\u003Cp\u003E6. 3D Scene Understanding\u003C\/p\u003E\n\u003Cp\u003ESemantic and instance segmentation of 3D scenes\u003C\/p\u003E\n\u003Cp\u003EObject detection and pose estimation in 3D\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n                                                \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ENature de l\u0026#039;\u00e9valuation\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EEvaluation Continue non Int\u00e9grale\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n  \u003C\/div\u003E\n\u003C\/div\u003E\n","settings":null}]