Spherical camera design & pose estimation algorithm
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Updated
Jun 9, 2020 - MATLAB
Spherical camera design & pose estimation algorithm
Implementation of Tomasi and Kanade SFM Factorization method, IJCV 1992.
The project involves projective geometry, geometric transformations, modelling of cameras, feature extraction, stereo vision, recognition and deep learning, 3d-modelling, geometry of surfaces and their silhouettes, tracking, and visualisation.
MATLAB implementations of various computer vision algorithms.
structure from motion (SfM) is the process of estimating the 3D structure of a scene from a set of 2D images. here I estimate the poses of a calibrated camera from two images, reconstruct the 3D structure of the scene up to an unknown scale factor, and then recover the actual scale factor by detecting an object of a known
Study of stereo photogrammetry implementation in Matlab using disparity map and feature triangulation to reconstruct the scene and Structure from Motion to estimate the camera pose
Academic and MOOC Projects in the areas of Robotics and Vision
Homologous point groups (point tracks) determination usable as the input of Structure from Motion (SfM).
Minimum Separation Vector Mapping (MSVM)
Computer Vision project : 3D reconstruction using a structure from motion algorithm
A MATLAB implementation of a full 3D Reconstruction pipeline for small-scale reconstructions using the functions provided by the Czech Technical University in Prague, Faculty of Electrical Engineering
Lorenzo Torresani's Structure from Motion Matlab code
Weekly graded projects of the "Computer Vision" course, ETH Zürich (Fall 2020).
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