3D AUGMENTED REALITY
Corso
A Padova
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Descrizione
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Tipologia
Corso
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Luogo
Padova
Final evaluation will be performed by means of a written exam and the development of a final project (to be document with a written report). Alternatively, the final project report can be replaced by two lab session reports on two lab experiences (chosen by the student).
Reports must be handed in at least one day before the final exam. The final score will be made of a weighted average of the evaluation of the written exam (50%) and the final project (50%).
The evaluation topics for the written exam will be clearly indicated during the course and in the course material.
Sedi e date
Luogo
Inizio del corso
Inizio del corso
Opinioni
Materie
- E-learning
- 3d
- 2D
Programma
1) Image formation and camera model
Perspective projection, Pin-hole camera, Thin lenses, Fish-eye lenses, simplified and general camera model, camera calibration.
2) Computation of salient points and features
Harris e Stephens method, Scale Invariant Feature Transform (SIFT), salient points correspondences.
3) Homographies
Computationof homographies (DLT), homographies and object detection, applications of 2D augmented reality.
4) Stereopsys
3D Triangulation, epipolar geometry, epipolar rectification, essential matrix and factorizzation, motion and structure from calibrated homography, local correspondence methods, window correspondence methods, accuracy-reliability trade-off, occlusions, global correspondence methods.
5) 3D reconstruction from other sensors
IR Structured-light depth sensors (MS Kinect v.1), Time-of-Flight depth sensors (MS Kinect v.2), active stereo sensors, laser scanners.
6) Non-calibrated reconstruction
Fundamental matrix and its computation, projective reconstruction from 2 and N views, incremental reconstruction, Structure-from-Motion, bundle adjustment.
7) Optical flow
Motion field: computation of motion and structure.
8) Orientation methods
Quaternions, orientation 2D-2D, orientatio 3D-3D: DLT and ICP methods, orientation 3D-2D.
b) Machine learning methods for augmented reality
9) Object detection and scene understanding
Image and 3D models features, image/object classification strategies, machine learning algorithms for object classification, Support Vector Machine for image/object classification, Deep Neural Networks for image/object classification, application of Deep Learning strategies to Human-computer interfaces (HCI).
c) visualization and graphical rendering
10) 3D displays, VR visors, and augmented reality devices
11) Rendering
Projective geometry and convention, ray tracing and ray casting, the radiance (or rendering) equation and its solution, illumination, the radiance solution by local methods: Phong and Cook-Torrance models, rasterization, the OpenGL pipeline.
12) Interactive augmented reality.
Introduction to Unity. Development of simple AR applications.
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3D AUGMENTED REALITY