Conference attendees are seated in a lecture hall, attentively listening to a presentation on "N-Point Linear Solver," part of a technical session at CVPR (Computer Vision and Pattern Recognition) conference. The slide displayed on the large screen outlines an algorithm for determining line and partial motion parameters, showcasing equations and matrices involved in the computation process. Some attendees take notes while others engage more passively, indicating a diverse level of engagement. The venue's industrial-style ceiling and lighting create a professional yet relaxed environment conducive to learning and networking. The large "CVPR" letters at the front underscore the significance of the conference in the field of computer vision.
Text transcribed from the image:
N-Point Linear Solver
Algorithm 1 Linear Solver for Line and Partial Motion Parameters
Input: A set of events & with rotated bearing vectors.
Output: Line parameters 6, and projected velocities up.
tf (ueue)+fe=0
knowns
[tf f
unknowns
= 0.
EXER6×1
•
•
Form matrix A from the set of events & by Eq. 7 and make sure that rank(A) ≥ 5.
Apply SVD on A and select the last column of V, denoted with x. Both x can be selected.
•Normalize ✰ by 4:6, the last three elements.
•
Recover e, u
from Eq. 9.
.
Recover e,
from Eq. 10. Both {e, u} and {-e, -u} can be selected.
Compile u =[0].
•
Compute e=ex e.
• Construct the rotation R₁ = [eee].
• Recover minimal line parameters 0₁ = (log (Re)).
tf fT
=AЄR5×6
e=x4:6
u = ×3×4:6
ue = 1:3 × ×4:6
u=1:3 × 4:6|| and e
*1:3 × ×4:6
=
||1:3 X 4:6||
Mil or minimal Car
CYB