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CS479: Machine Learning for 3D Data

Minhyuk Sung, KAIST, Spring 2026


Teaser1

Time & Location

Time: Mon/Wed 1:00 p.m. - 2:15 p.m. (KST)
Location: E3-5 Room 210

Description

3D Data are widely used in many applications in computer vision, computer graphics, and robotic, such as autonomous driving, AI-assisted 3D object/scene design, augmented reality, and physical robot interaction. Along with the recent increasing demands on processing and analyzing such 3D data, there has been tremendous progress in developing novel technologies, especially based on deep learning. In this course, we will cover the recent advances in machine learning techniques for 3D data and also discuss the remaining challenges.

Prerequisites

Required

  • CS371: Introduction to Deep Learning
  • CS380: Introduction to Computer Graphics
  • CS484: Introduction to Computer Vision

Course Staff

Instructor: Minhyuk Sung (mhsung@kaist.ac.kr)

Course Assistants:

Past Years

Grading

AI Coding Assistant Tool Policy

You are allowed to use AI coding assistant tools (e.g., Claude, Codex, Cursor, etc.) for your programming assignments and projects. Utilizing AI coding assistant tools will not be deemed as plagiarism. However, it is still strictly prohibited to directly copy code from the Internet or from someone else. Doing so will lead to a score of zero and a report to the university.

Important Dates

ALL ASSIGNMENTS ARE DUE 23:59 KST. (Subject to Change)

  • Assignment 1 Submission Due: March 31 (Tuesday), 23:59 KST
  • Team Sign-Up Due: April 1 (Wednesday), 23:59 KST
  • Assignment 2 Submission Due: April 28 (Tuesday), 23:59 KST
  • Assignment 3 Submission Due: May 12 (Tuesday), 23:59 KST
  • Assignment 4 Submission Due: May 26 (Tuesday), 23:59 KST
  • 3D Segmentation Competition Midterm Submission Due: Apr 30 (Thursday), 23:59 KST
  • 3D Segmentation Competition Final Submission Due: May 9 (Saturday), 23:59 KST
  • 3D Rendering Contest Submission Due: June 6 (Saturday), 23:59 KST

Schedule

(Subject to Change)

Week Mon Topic Wed Topic
1 Mar 2 No Class (Substitute Holiday for
the Independence Movement Day)		 Mar 4 Course Introduction
Slides
2 Mar 9 3D Representations
Slides
Recording		 Mar 11 Point Clouds 1
Slides
Recording
3 Mar 16 Assignment 1 Session:
PointNet		 Mar 18 Point Clouds 2
4 Mar 23 Implicit Neural Representations Mar 25 Image-to-3D 1:
Camera Model
5 Mar 30 Image-to-3D 2:
Epipolar Geometry		 Apr 1 Neural Radiance Fields (NeRF)
6 Apr 6 Hybrid Representations Apr 8 Assignment 2 Session:
NeRF
7 Apr 13 Guest Lecture 1
Clément Jambon
Ph.D. Student at MIT		 Apr 14 (Tue) Guest Lecture 2
Qixing Huang
Associate Professor at UT Austin
Apr 14 (Tue) 4:00 p.m.
8 Apr 20 Midterm Exam Apr 22 No Class (Midterm Week)
9 Apr 27 Gaussian Splatting 1 Apr 29 Assignment 3 Session:
Gaussian Splatting
10 May 4 Gaussian Splatting 2 May 6 No Class (Break)
11 May 11 Representation Conversion 1 May 13 Assignment 4 Session:
Marching Squares
12 May 18 Representation Conversion 2 May 20 Mesh Deformation
13 May 26 No Class (Substitute Holiday for
Buddha's Birthday)		 May 27 Guest Lecture 3
Seungchan Kim
Ph.D. Student at CMU
14 Jun 1 3D Generation /
Course Wrap-Up		 Jun 3 No Class (Local Elections)
15 Jun 8 Project Presentations 1 Jun 10 Project Presentations 2
16 Jun 15 Final Exam Jun 17 No Class (Final Week)

  1. Teaser image credits (from left to right):
    Mildenhall et al., NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, ECCV 2020.
    https://huggingface.co/blog/gaussian-splatting
    Hwang and Sung, Occupancy-Based Dual Contouring, SIGGRAPH Asia 2024.
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