# Understanding Depth Map / Depth Image / Disparity Map / Disparity Image

Please help me understand the difference between a depth map, a depth image, a disparity map, and a disparity image as I am very confused about the relation between them and how each is used in practice.

After reading things online, my current understanding is that a depth map holds actual distance values in say meters at each pixel location while the depth image holds at each pixel location, a pixel intensity representation of that distance. Is this true?

For a 16-bit uint depth image for example if the depth image holds at position (i, j) an intensity value of 65535, the corresponding depth map will have at position (i, j) a distance of 65535/512 = 128 meters. Is this true?

If what I stated above is true, this would suggest that one unit of pixel intensity in the depth image represents 1/512 meters and I was wondering why is it that one unit of the pixel represents 1/512 meters and not say 1/1024 meters of even 1/128 meters for example. How do we determine that conversion factor.

when we say that depth = (baseline * focal length) / disparity), do we mean that:

depth_image = (baseline * focal length) / disparity_image) in the pixel intensity domain

or that do we mean instead that:

depth_map = (baseline * focal length) / disparity_map) in the distance in meters domain.

That is because the proportionality factor k = (baseline * focal length)will be in a unit of square meters m^2 which will require us to always do such conversion in the distance (meters) domain so that the equation is balanced.

If we were to do this in the pixel domain instead and write the equation as:

depth_image * disparity_image = baseline * focal length

we will have px^2 = m^2 which is not balanced metric wise pending some factor to either get px^2 = px^2 or m^2 = m^2.

In research papers, when deep learning methods are used to predict depth or disparity, are they directly predicting depth images and disparity images i.e. the pixel intensity values? or are they regressing the actual distances in meters values and then converting it back to pixel values for visualization.

If I have a depth image uint 16-bit from some dataset with not other information, is it always true that because I able to read in that depth image and display it, it must be the pixel intensity values of the depth that such dataset is providing.

If I wanted to go from such depth image from the dataset to a disparity image, would I just need to apply disparity_image = (baseline * focal length) / depth_image in the pixel intensity domain or do I need to first convert the given depth image to distances (meters), then apply the formula, then convert the obtained disparity map (in meters) to pixel value before I can use it to train any models.

Thank you very much for you help.

• Can you provide a link within your question to the sources where you found the definitions and examples you refer to? Feb 26, 2022 at 0:27
• Thank you for you reply @GrapefruitIsAwesome. I gathered these explanations by reading various posts, questions and blogs on places such as stack-overflow, medium, research-gate. I also noticed that some people tend to use the term depth map and depth image interchangeably to mean the same thing. I even came across a post that was discussing depth ranges as a separate and related concept. The same goes for disparity. I got really confused and posted my understanding in the question which I assumed may be wrong. Feb 26, 2022 at 7:31