Reconstructing Kite Position and Rotation from a Single Camera Image

In summary, the conversation discusses the possibility of calculating the exact position and rotation of a kite in a vectorspace defined by a camera, given the dimensions of the kite and the specifications of the camera. The speaker mentions trying to use trigonometry but failing and wondering if there is an easier way using matrices. They also mention having precise pixel values for the four corners of the kite in the photo.
  • #1
velcrome
2
0
Image a kite (1 m wide, 3 m high, both crossing at a third of the height).
Also imagine a digital camera (800x600 pixel with a horizontal field of view of 45°).

After launching the kite a photo is taken with the camera.

How can I easily calculate the exact position *and* rotation of the kite in a vectorspace defined by the camera at the moment the picture was taken?
I tried with trigonometry but failed. I assume there is an easier way with matrices but i don't know how. I appreciate any help.

*velcrome
 
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  • #2
??Given what information?
 
  • #3
there are four corners of the kite with precise pixel values in the picture.

and of course, the dimensions of the kite and the specifications of the camera is known.
 

Related to Reconstructing Kite Position and Rotation from a Single Camera Image

What is the "Inverse Projection Problem"?

The Inverse Projection Problem (IPP) is a fundamental problem in computer vision and machine learning. It involves determining the 3D shape and pose of an object from its 2D image projection or representation.

Why is the "Inverse Projection Problem" important?

The IPP is important because it has numerous applications in areas such as object recognition, augmented reality, and robotics. Accurately solving the IPP can enable machines to interact with the physical world and perform complex tasks.

What are the challenges of solving the "Inverse Projection Problem"?

One of the main challenges of the IPP is the ill-posed nature of the problem. There are infinite 3D shapes that can result in the same 2D image projection, making it difficult to determine the exact 3D shape. Additionally, factors such as lighting, occlusions, and noise can also affect the accuracy of the solution.

What are some approaches to solving the "Inverse Projection Problem"?

There are several approaches to solving the IPP, including geometric methods, statistical methods, and deep learning methods. Geometric methods involve using mathematical equations and geometric relationships between 2D and 3D points to estimate the object's shape and pose. Statistical methods use probabilistic models to find the most likely 3D shape and pose given the 2D image. Deep learning methods use neural networks to learn the mapping between 2D and 3D representations.

What are some limitations of current solutions to the "Inverse Projection Problem"?

Current solutions to the IPP still have limitations, such as being sensitive to lighting conditions, not being able to handle complex or non-rigid objects, and requiring large amounts of training data. Additionally, most solutions are specific to certain types of objects and may not generalize well to new objects or scenarios.

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