pinhole camera

Table of Contents 1. Introduction: Perspective-n-Point (PnP) problem 2. Degrees of freedom 3. Mapping 3D points to camera coordinates 4. Retrieving the camera pose 4.1. Noiseless scenario 4.2. Noisy scenario 4.3. Handling the reflection case 4.4. Algorithm 5. Example 6. Conclusion 7. References 1. Introduction: Perspective-n-Point (PnP) problem We have seen in an earlier article how we can estimate the homography matrix $H$ that characterizes the projective transform between two images....

Table of Contents 1. Introduction 2. Conics 2.1. Tangency 2.2. Duality 2.3. Pole-polar relationship 2.4. Conjugacy 3. Undoing the projective distortion 3.1. 2D projective space 3.1.1. Angles between rays 3.1.2. The line at infinity 3.1.3. The circular points 3.2. 3D projective space 3.2.1. The plane at infinity 3.2.2. The absolute conic 3.2.3. Angles between rays 3.2.4. The dual absolute quadric 3.2.5. Angles between planes 4. Camera calibration 4.1 Angle between rays 4....

Table of Contents 1. Introduction 2. Via geometric features 2.1. From points/lines 2.1.1. Problem formulation 2.1.2. Noise amplification: the horizon line 2.1.3. Solution: least squares estimator 2.2. From conics 2.3. From multiple features 3. Via a pair images 4. Via ML model 5. Summary 6. References 1. Introduction So far, we have figured out how to: Mathematically characterize the transform between the 3D world and a 2D image of it Map different types of objects between the two domains However, one might wonder: how do we actually compute the homography matrix that fully describes that transform in the first place?...

Table of Contents 1. Motivation: sports analytics 2. Points 3. Lines 4. Conics 4.1. Projection 4.2. Distortion: Objects behind the camera plane 5. Images 5.1. Projection via object decomposition 6. References 1. Motivation: sports analytics At this point, we know how to mathematically characterise the mapping between the 3D world and a 2D image capturing it. So it seems natural to wonder: what can we do with it? In this post, I will focus on a use case that I happen to be familiar with, but there are many others you can think of....

Table of Contents 1. Pinhole camera model 2. Intrinsic matrix 2.1. Setup 2.2. Homogeneous coordinates 2.3. Accounting for distortions 2.3.1. Digital images 2.3.2. Rephotographing Images 3. Extrinsic matrix 4. Homography matrix 5. References 1. Pinhole camera model When we capture something on camera, there is an interesting phenomenon going on: compression. We are taking a photograph of a 3D world, and capturing it in a 2D image. This 3D→2D space mapping inevitably leads to information loss....