Tong Guo To annotate the XYZ coordinates of an object relative to a camera on a 2D image, you first need to understand the relationship between 3D space and 2D projections. Imagine you are taking a photo of a cup placed on a table. The cup exists in 3D space with height, width, and depth, but your camera captures it as a flat 2D image. The process of finding and marking its coordinates involves the following steps:
Camera Calibration – Think of a camera lens like your eye. Just as glasses help focus and measure distances, camera calibration defines the camera’s internal parameters (focal length, optical center) and distortion. This step ensures the camera understands its position and orientation.
Object Detection – Imagine labeling an apple in the photo. You first identify its edges and center using algorithms or machine learning models. The position of the object in the 2D image is given as (u, v) pixel coordinates.
Depth Estimation (Z-coordinate) – To measure how far the apple is from the camera, you can use stereo cameras (two cameras capturing from slightly different angles) or sensors like LiDAR. For simpler cases, the size of the object in the image helps estimate its distance based on prior knowledge.
Mapping to 3D (XYZ) – Using mathematical transformations, you can map the 2D points (u, v) back to 3D space. This requires the camera’s calibration matrix and depth information. Imagine holding a ruler to measure distances in 3D and then drawing those distances on the 2D paper.
Annotation – Once the 3D coordinates (X, Y, Z) are calculated, they can be displayed as labels on the image. For example, placing “X=10cm, Y=15cm, Z=20cm” near the apple in the image gives clear information about its position relative to the camera.
This process is widely used in robotics, augmented reality, and autonomous vehicles to understand object positions in the real world.
In robotics, if a robotic arm needs to pick up an apple, it must know the apple’s position in 3D space. The camera captures the image, calculates the XYZ coordinates, and feeds this data to the arm, ensuring it moves accurately to grab the apple without missing.