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courses.html
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<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<meta name="description" content="Course offerings in computer vision at Carnegie Mellon.">
<title>Computer Vision @ CMU</title>
<link href='http://fonts.googleapis.com/css?family=EB+Garamond' rel='stylesheet' type='text/css'>
<link href='http://fonts.googleapis.com/css?family=Roboto+Condensed:700' rel='stylesheet' type='text/css'>
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type='text/css'>
<link rel="icon" type="image/x-icon" href="./assets/ri-favicon.ico">
<link rel="stylesheet" href="./style.css">
</head>
<body class="page page-id-16 page-parent page-child parent-pageid-24 page-template-default">
<div id="wrapper">
<div id="header">
<div id="logo"><a href="./index.html"><img alt="Computer Vision @ Carnegie Mellon" src="./assets/logo.svg"></a>
</div>
<div id="navBar">
<a href="./index.html">People</a>
<a href="./research.html">Research</a>
<a href="./courses.html">Courses</a>
</div>
</div>
<div id="main" role="main">
<div class="contentItem">
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<table class="course" id="15463">
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<td class="courseDescription">
<h2 class="courseTitle"> 15-463, 15-663, 15-862 : Computational Photography </h2>
Computational photography is the convergence of computer graphics, computer vision, optics and imaging. Its role is to overcome the limitations of traditional cameras, by combining imaging and computation to enable new and enhanced ways of capturing, representing, and interacting with the physical world. This advanced undergraduate course provides a comprehensive overview of the state of the art in computational photography. At the start of the course, we will study modern image processing pipelines, including those encountered on mobile phone and DSLR cameras, and advanced image and video editing algorithms. Then we will continue to learn about the physical and computational aspects of tasks such as 3D scanning, coded photography, lightfield imaging, time-of-flight imaging, VR/AR displays, and computational light transport. Near the end of the course, we will discuss active research topics, such as creating cameras that capture video at the speed of light, cameras that look around walls, or cameras that can see below skin.
<p class="offeringList"><a href="http://graphics.cs.cmu.edu/courses/15-463/">Course Website</a></p>
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<table class="course" id="15468">
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<h2 class="courseTitle"> 15-468, 15-668, 15-868 : Physics-based Rendering </h2>
This course is an introduction to physics-based rendering at the advanced undergraduate and introductory graduate level. During the course, we will cover fundamentals of light transport, including topics such as the rendering and radiative transfer equation, light transport operators, path integral formulations, and approximations such as diffusion and single scattering. Additionally, we will discuss state-of-the-art models for illumination, surface and volumetric scattering, and sensors. Finally, we will use these theoretical foundations to develop Monte Carlo algorithms and sampling techniques for efficiently simulating physically-accurate images. Towards the end of the course, we will look at advanced topics such as rendering wave optics, neural rendering, and differentiable rendering.
<p class="offeringList"><a href="http://graphics.cs.cmu.edu/courses/15-468/">Course Website</a></p>
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<table class="course" id="16385">
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<h2 class="courseTitle"> 16-385 : Computer Vision </h2>
This course provides a comprehensive introduction to computer vision. Major topics include image processing, detection and recognition, geometry-based and physics-based vision and video analysis. Students will learn basic concepts of computer vision as well as hands on experience to solve real-life vision problems.
<p class="offeringList"><a href="http://16385.courses.cs.cmu.edu/spring2022/">Course Website</a></p>
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<table class="course" id="16824">
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<h2 class="courseTitle"> 16-824 : Visual Learning and Recognition </h2>
This graduate-level computer vision course focuses on representation and reasoning for large amounts of data (images, videos, and associated tags, text, GPS locations, etc.) toward the ultimate goal of understanding the visual world surrounding us. We will be reading an eclectic mix of classic and recent papers on topics including Theories of Perception, Mid-level Vision (Grouping, Segmentation, Poses), Object and Scene Recognition, 3D Scene Understanding, Action Recognition, Contextual Reasoning, Joint Language and Vision Models, Deep Generative Models, etc. We will be covering a wide range of supervised, semi-supervised and unsupervised approaches for each of the topics above.
<p class="offeringList"><a href="https://visual-learning.cs.cmu.edu/">Course Website</a></p>
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<table class="course" id="16889">
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<h2 class="courseTitle"> 16-889 : Learning for 3D Vision </h2>
Any autonomous agent we develop must perceive and act in a 3D world. The ability to infer, model, and utilize 3D representations is therefore of central importance in AI, with applications ranging from robotic manipulation and self-driving to virtual reality and image manipulation. While 3D understanding has been a longstanding goal in computer vision, it has witnessed several impressive advances due to the rapid recent progress in (deep) learning techniques. The goal of this course is to explore this confluence of 3D Vision and Learning-based methods.
<p class="offeringList"><a href="https://learning3d.github.io/">Course Website</a></p>
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</div> <!-- content -->
</div>
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<div id="footWrapper">
<div id="footer">
<span id="footerLogo"><a href="http://www.cmu.edu/index.shtml"><img src="./assets/cmu.svg"
alt="Carnegie Mellon University"></a></span>
<span id="footerAddress"><a
href="https://www.google.com/maps/place/Carnegie+Mellon+University/@40.442492,-79.942553,17z/data=!3m1!4b1!4m2!3m1!1s0x8834f21f58679a9f:0x88716b461fc4daf4">5000
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