<p>In computer vision, the adjectives <em>dense</em> and <em>sparse</em> are used in a variety of tasks (e.g. <a href="https://en.wikipedia.org/wiki/Optical_flow" rel="nofollow noreferrer">optimal flow</a>), but they are commonly used in the context of the <a href="https://www.mia.uni-saarland.de/zimmer/phd-thesis" rel="nofollow noreferrer">correspondence problem</a>, which is the problem of finding a map (or correspondence) between pixels of two images (e.g. two successive frames of a video). In this context, these adjectives thus refer to the number of pixels of the image that are used to solve this specific task. A <em>dense correspondence</em> is thus a correspondence between two images using all (or, at least, <em>many</em>) pixels. In other words, a dense correspondence attempts to map all (or many) pixels of an image to all (or many) pixels of another image.</p>

<p>In the case of <a href="https://arxiv.org/pdf/1802.00434.pdf" rel="nofollow noreferrer">DensePose</a>, the correspondences are between RGB images and surface-based representation of the human body (the figures of the paper illustrates this).</p>

CC BY-SA 4.0
2019-07-09T09:26:33.587Z
2019-07-09T09:32:01.153Z
2019-07-09T09:32:01.153Z
nbro
<p>I've recently come across an amazing work for human pose estimation: <a href="https://arxiv.org/pdf/1802.00434.pdf" rel="nofollow noreferrer">DensePose: Dense Human Pose Estimation In The Wild</a> by Facebook. </p>

<p>In this work, they have tackled the task of dense human pose estimation using discriminative trained models.</p>

<p>I do understand that "correspondence" means how well pixels in one image correspond to pixels in the second image (specifically, here - 2D to 3D).</p>

<p>But what does "dense" means in this case?</p>

<machine-learning><computer-vision><terminology><papers>
What is "dense" in DensePose?
Alex Goft
# What is "dense" in DensePose?

(Asked by: username_0 on 2019-07-09)

I've recently come across an amazing work for human pose estimation: [DensePose: Dense Human Pose Estimation In The Wild](https://arxiv.org/pdf/1802.00434.pdf) by Facebook.

In this work, they have tackled the task of dense human pose estimation using discriminative trained models.

I do understand that "correspondence" means how well pixels in one image correspond to pixels in the second image (specifically, here - 2D to 3D).

But what does "dense" means in this case?

**Tags:** machine-learning, computer-vision, terminology, papers


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## 1 answers

### Answer by username_1 on 2019-07-09. Score: 0 | Accepted answer

In computer vision, the adjectives *dense* and *sparse* are used in a variety of tasks (e.g. [optimal flow](https://en.wikipedia.org/wiki/Optical_flow)), but they are commonly used in the context of the [correspondence problem](https://www.mia.uni-saarland.de/zimmer/phd-thesis), which is the problem of finding a map (or correspondence) between pixels of two images (e.g. two successive frames of a video). In this context, these adjectives thus refer to the number of pixels of the image that are used to solve this specific task. A *dense correspondence* is thus a correspondence between two images using all (or, at least, *many*) pixels. In other words, a dense correspondence attempts to map all (or many) pixels of an image to all (or many) pixels of another image.

In the case of [DensePose](https://arxiv.org/pdf/1802.00434.pdf), the correspondences are between RGB images and surface-based representation of the human body (the figures of the paper illustrates this).


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