Geometric Computer Vision: Difference between revisions

Geometric Computer Vision (view source)

687 bytes added , 1 April 2021

5,321

edits

@@ Line 281: / Line 281: @@
 Assume <math>E(x(y), y(t), t) = constant</math>
+==Structure from Motion Pipeline==
+===Calibration===
+# Step 1: Feature Matching
+===Fundamental Matrix and Essential Matrix===
+# Step 2: Estimate Fundamental Matrix F
+#* <math>x_i'^T F x_i = 0</math>
+#* Use SVD to solve for x from <math>Ax=0</math>: <math>A=U \Sigma V^T</math>. The solution is the last singular vector of <math>V</math>.
+#* Essential Matrix: <math>E = K^T F K</math>
+#* '''Fundamental matrix has 7 degrees of freedom, essential matrix has 5 degrees of freedom'''
+===Estimating Camera Pose===
+Estimating Camera Pose from E
+Pose P has 6 DoF. Do SVD of the essential matrix to get 4 potential solutions.
+You need to do triangulation to select from the 4 solutions.
 ==Projects==