Structure from Motion (SfM) is the process of estimating the 3-D structure of a scene from a set of 2-D images. For more details, see Implement Visual SLAM in MATLAB.
|Detect BRISK features and return |
|Detect corners using FAST algorithm and return |
|Detect corners using Harris–Stephens algorithm
and return |
|Detect corners using minimum eigenvalue algorithm and
|Detect MSER features and return |
|Detect scale invariant feature transform (SIFT) features and return |
|Detect SURF features and return |
|Manage data for structure-from-motion, visual odometry, and visual SLAM|
|Manage 3-D to 2-D point correspondences|
|Object for storing intrinsic camera parameters|
|3-D rigid geometric transformation|
|3-D affine geometric transformation|
|Estimate essential matrix from corresponding points in a pair of images|
|Estimate fundamental matrix from corresponding points in stereo images|
|Estimate camera pose from 3-D to 2-D point correspondences|
|Compute relative rotation and translation between camera poses|
|Object for storing matching points from multiple views|
|Find matched points across multiple views|
|3-D locations of undistorted matching points in stereo images|
|3-D locations of world points matched across multiple images|
Determine location and orientation of a camera by analyzing a sequence of images.
Visual simultaneous localization and mapping (vSLAM).
Specify pixel Indices, spatial coordinates, and 3-D coordinate systems
Choose functions that return and accept points objects for several types of features
Learn the benefits and applications of local feature detection and extraction.
Estimate three-dimensional structures from two-dimensional image sequences
Understand the visual simultaneous localization and mapping (vSLAM) workflow and how to implement it using MATLAB.