I am currently working on a project involving computer vision and have encountered a challenge for which I seek your insights and assistance.
Background: In my scenario, I have a calibrated camera for which I possess the intrinsic parameters, encapsulated within the camera matrix. Additionally, through my setup, I have collected a set of 3D points from the real world and their corresponding 2D projections captured in an image through this camera. My objective is to accurately determine the camera’s pose and orientation relative to the observed scene or object.
Problem Statement: Despite having the camera matrix and the 3D-2D point correspondences, I am grappling with the methodology to effectively calculate the camera’s pose, specifically its position and orientation (rotation) in the world coordinate system.
- An understanding of the theoretical approach behind calculating the pose. Is this what is referred to as solving the PnP (Perspective-n-Point) problem?
- Any robust algorithm suggestions or best practices that are typically employed in the industry for such calculations. I have come across methods like EPnP, DLT, and iterative optimization techniques, but I am unsure of their applicability or efficiency in this context.
- Practical examples or pseudo-code to illustrate the process. While I understand the theoretical aspect may be complex, having a practical, code-oriented guide would significantly ease the implementation phase.
- Common pitfalls or errors that I should be vigilant about during the implementation. Are there any specific aspects regarding the quality of the 3D-2D correspondences, the number of points required, or certain conditions that might skew the results?