computer-vision-course/pr_397/en/unit8/introduction/introduction.html

<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Introduction&quot;,&quot;local&quot;:&quot;introduction&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Unit Overview&quot;,&quot;local&quot;:&quot;unit-overview&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}">
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		<link rel="modulepreload" href="/docs/computer-vision-course/pr_397/en/_app/immutable/chunks/index.514d62da.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Introduction&quot;,&quot;local&quot;:&quot;introduction&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Unit Overview&quot;,&quot;local&quot;:&quot;unit-overview&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>  <h1 class="relative group"><a id="introduction" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#introduction"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Introduction</span></h1> <p data-svelte-h="svelte-flk9gz">Apart from videos, about which we talked in the last chapter, another common form of visual data comes in the 3-dimensional form.
While for 2D images we usually have the two dimensions, commonly labelled as <em>x</em> and <em>y</em>, for 3D images we have three dimensions, referred to as <em>x</em>, <em>y</em> and <em>z</em>.</p> <p data-svelte-h="svelte-khwo5c">“But wait,” I hear you say, “videos also have three dimensions!” That is completely correct - videos have the two spatial dimensions, <em>x</em> and <em>y</em>, and the temporal dimension, <em>t</em>. The difference with 3D data is that here all three dimensions are of a spatial nature. This helps us to create a better model of our world and our perceptive capabilities. That is why one very common field for 3D applications nowadays is Mixed Reality applications, in which we try to merge the digital and analog worlds.</p>  <h2 class="relative group"><a id="unit-overview" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#unit-overview"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Unit Overview</span></h2> <p data-svelte-h="svelte-1fddmko">You will learn more about applications of 3D Computer Vision in the first chapter after this introduction. Right after that, we will take a look at the historical developments of 3D applications - all the way from the 19th century to today.</p> <p data-svelte-h="svelte-l6a1od">After these general topics, we’ll dive right into the terminologies and concepts with three chapters about camera models, linear algebra and different representations.</p> <p data-svelte-h="svelte-1xp2eml">We are following up the theory with some proper fields of use for 3D Computer Vision. Starting off with Novel View Synthesis, followed by Stereo Vision and finishing this unit (for now) with one of the most popular applications right now - Neural Radiance Fields (NeRFs).</p> <p data-svelte-h="svelte-fyqqws">Ready? Then get out your 3D goggles and lets learn! 🌟</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit8/introduction/introduction.mdx" target="_blank"><span data-svelte-h="svelte-1kd6by1">&lt;</span> <span data-svelte-h="svelte-x0xyl0">&gt;</span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a>  <p></p> 
			
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