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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<link rel="icon" type="image/png" href="data/seal_icon.png">
<title>Learning for 3D Vision</title>
<meta http-equiv="content-type" content="text/html; charset=utf-8" />
<link href="data/default.css" rel="stylesheet" type="text/css" />
<meta property='og:title' content='Learning for 3D Vision' />
<meta property='og:url' content='https://learning3d.github.io/' />
<meta property='og:image' content='https://learning3d.github.io/data/teaserim.png' />
<meta property="og:type" content="website" />
<script src="https://unpkg.com/htmlincludejs"></script>
</head>
<body>
<div id="header">
<div id="logo">
<include src="./header.html"></include>
<h2 class="logo-header">
<center>
<span style="font-size:92%;color:#777;font-weight:normal">Instructor: <a href="https://www.cs.cmu.edu/~igkioule/">Ioannis Gkioulekas</a>, <a href="https://shubhtuls.github.io">Shubham Tulsiani</a> </span><br/>
<span style="font-size:92%;color:#777;font-weight:normal">Monday and Wednesday, 2:00-3:20pm, TEP 1403</span><br/>
</center>
</h2><br>
</div>
<div id="splash">
<center><img src="data/teaserim.png" alt="" width="1000" /></center>
</div>
<div id="content">
<h2>Course Description</h2>
<p>
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. In particular, this course will cover topics including -
<br>
<left>
<table id="topics" style="width:80%">
<tr>
<td colspan="2">
<left>• Explicit, Implicit, and Neural 3D Representations</left>
</td>
<td>
<left>• Differentiable Rendering</left>
</td>
</tr>
<tr>
<td colspan="2">
<left>• Single-view 3D Prediction: Objects, Scenes, and Humans</left>
</td>
<td>
<left>• Neural Rendering</left>
</td>
</tr>
<tr>
<td colspan="3">
<left>• Multi-view 3D Inference: Radiance Fields, Multi-plane Images, Implicit Surfaces, etc.
</left>
</td>
</tr>
<tr>
<td>
<left>• Generative 3D Models</left>
</td>
<td>
<left>• Shape Abstraction</left>
</td>
<td>
<left>• Mesh and Point cloud processing</left>
</td>
</tr>
</table>
</left>
</p>
<h2>Format and Prerequisites</h2>
<p>
The course will be lecture-based, and the grades will primarily be determined by assignments and a final
project. The course will require as background good coding skills, and an understanding of basics in Computer Vision (e.g. image formation, ray optics) and Machine Learning (e.g. optimization, neural networks).
<h2>Course Staff</h2>
<p>Please use the course <a href="https://piazza.com/cmu/spring2025/16825a/home">Piazza page</a> for all communication with course staff. See this <a href="https://docs.google.com/spreadsheets/d/e/2PACX-1vRiyC_d-HLyunAjPBF272WRsyaRxTWBzJiXHf8YLHwVz4-H6NPrVW_r4t-f-D4yKc56NDV6nph_egn6/pubhtml?gid=0&single=true">office hour schedule</a> for in-person meetings. </p>
<h3> Course Instructor </h3>
<div class="instructor">
<a href="https://www.cs.cmu.edu/~igkioule/">
<div class="instructorphoto"><img src="./data/instructor_2.jpg"></div>
<div>Ioannis Gkioulekas</div>
</a>
</div>
<div class="instructor">
<a href="https://shubhtuls.github.io">
<div class="instructorphoto"><img src="./data/instructor.jpg"></div>
<div>Shubham Tulsiani</div>
</a>
</div>
</br>
<br>
<h3> Teaching Assistants </h3>
<div class="instructor">
<a href="https://www.ri.cmu.edu/ri-people/abhishek-warrier/">
<div class="instructorphoto"><img src="data/tas/abhishek.jpg"></div>
<div>Abhishek Warrier</div>
</a>
</div>
<div class="instructor">
<a href="https://jaykarhade.github.io/">
<div class="instructorphoto"><img src="data/tas/jay.jpeg"></div>
<div>Jay Karhade</div>
</a>
</div>
<div class="instructor">
<a href="https://lucas-707.github.io/">
<div class="instructorphoto"><img src="data/tas/lucas.jpeg"></div>
<div>Lucas Wu</div>
</a>
</div>
<div class="instructor">
<a href="https://sally-chen.github.io/">
<div class="instructorphoto"><img src="data/tas/sally.png"></div>
<div>Sally Chen</div>
</a>
</div>
<div class="instructor">
<a href="https://yanbo-xu.netlify.app/">
<div class="instructorphoto"><img src="data/tas/yanbo.jpg"></div>
<div>Yanbo Xu</div>
</a>
</div>
<br>
</br></br>
<h2>Related Courses</h2>
<br>
If you found this course useful, you may also be interested in the following related courses: <br><br>
<a href="https://sites.google.com/berkeley.edu/cs294-173/"> Learning for 3D Vision</a> by Angjoo Kanazawa, UC Berkeley <br>
<a href="https://courses.engr.illinois.edu/cs598dwh/fa2021/"> 3D Vision</a> by Derek Hoiem, UIUC <br>
<a href="http://graphics.cs.cmu.edu/courses/15-468/"> Physics-based Rendering</a> by Ioannis (Yannis) Gkioulekas, CMU <br>
<a href="https://www.scenerepresentations.org/courses/inverse-graphics/"> Machine Learning for Inverse Graphics</a> by Vincent Sitzmann, MIT <br>
<a href="https://geometric3d.github.io/"> Geometry-based Methods in Vision</a>, CMU <br>
<a href="https://haosulab.github.io/ml-meets-geometry/WI21/"> Machine Learning meets Geometry</a> by Hao Su, UCSD <br>
<br>
<strong>Previous offerings</strong>:
<a href="https://learning3d.github.io/spring24/"> Spring 2024</a>,
<a href="https://learning3d.github.io/spring23/"> Spring 2023</a>,
<a href="https://learning3d.github.io/spring22/"> Spring 2022</a>
<br></br></br>
<div style="clear: both;"> </div>
</div>
</body>
</html>