Neural Radiance Fields

Digital Drawing

Sampling

Aliasing

Antialiasing

Transforms

Texture Mapping

Rasterization Pipeline

Bezier Curves

Splines

Geometry

Mesh Representations & Geometry Processing

Ray Tracing

Radiometry & Photometry

Monte Carlo Integration

Global Illumination

Material Modeling

Camera & Lenses

Animation / Kinematics

Color Science

Image Sensors

Digital Image Processing

Light Fields

Virtual Reality

CS 184/284a: Computer Graphics and Imaging UC Berkeley Spring 2022 This course provides a broad introduction to computer graphics, covering modeling, rendering, animation, and imaging. It emphasizes the mathematical and geometric aspects of graphics and requires a data structures course and programming ability. Covered concepts range from 2D and 3D transformations to image processing. This course provides a broad introduction to the fundamentals of computer graphics. The main areas covered are modeling, rendering, animation and imaging. Topics include 2D and 3D transformations, drawing to raster displays, sampling, texturing, antialiasing, geometric modeling, ray tracing and global illumination, animation, cameras, image processing and computational imaging. There will be an emphasis on mathematical and geometric aspects of graphics, and the ability to write complete 3D graphics programs.  A data structures course (e.g. CS 61B), C/C++ programming ability, fluency with development environment and debugging programs, knowledge of vectors, matrices basic linear algebra, calculus and trigonometry. Helpful: exposure to statistics, signal processing, and the Fourier transform. The primary source for the course will be the website, lectures, and section. Suggested supplementary reading and resources will be posted on the course readings page. The following textbooks are recommended, but optional, resources for you in this course and beyond:

### *Physically Based Rendering: From Theory to Implementation (Third Edition):*

Authors: Matt Pharr and Greg Humphreys

- This book (PBRT) is the book for learning about modern ray tracing techniques. It has a [great website](http://www.pbrt.org/) with full source code online for an advanced physically-based ray tracer. It even won an [Oscar](https://www.youtube.com/watch?v=7d9juPsv1QU) for its impact on the film industry!
- PBRT is available free online for you through Berkeley login: ([Second edition](http://www.sciencedirect.com/science/book/9780123750792), [Third edition](http://www.sciencedirect.com/science/book/9780128006450))
- Also available on [Amazon](https://smile.amazon.com/gp/product/0128006455/)

### *Fundamentals of Computer Graphics*

Authors: Pete Shirley and Steve Marschner with Michael Ashikhmin, Michael Gleicher, Naty Hoffman, Garrett Johnson, Tamara Munzner, Erik Reinhard, Kelvin Sung, William B. Thompson, Peter Willemsen, and Bryan Wyvill

- Available on [Amazon](http://www.amazon.com/Fundamentals-Computer-Graphics-Peter-Shirley/dp/1568814690)

### *Computer Graphics: Principles and Practice*

Authors: John F. Hughes, Andries van Dam, Morgan McGuire, David F. Sklar, James D. Foley, Steven K. Feiner, and Kurt Akeley

- Available on [Amazon](http://www.amazon.com/Computer-Graphics-Principles-Practice-3rd/dp/0321399528/ref=sr_1_2?s=books&ie=UTF8&qid=1440872554&sr=1-2&keywords=foundations+of+3d+computer+graphics)

CS 184/284a: Computer Graphics and Imaging

Computer Graphics

Camera Models

Camera Calibration

Single View Metrology

Epipolar Geometry

Stereo Systems

Structure from Motion

Active Stereo

Volumetric Stereo

Fitting

Representation Learning

Monocular Depth Estimation

Feature Tracking

Scene Flow

Optimal Estimation

Optical Flow

CS 231A: Computer Vision, From 3D Reconstruction to Recognition Stanford University Winter 2023 This course introduces concepts and applications in computer vision, focusing on geometry and 3D understanding. It covers topics like filtering, edge detection, segmentation, clustering, shape reconstruction from stereo, and high-level visual topics. Knowledge of linear algebra, basic probability, and statistics is required. An introduction to concepts and applications in computer vision primarily dealing with geometry and 3D understanding. Topics include: cameras and projection models, low-level image processing methods such as filtering and edge detection; mid-level vision topics such as segmentation and clustering; shape reconstruction from stereo; high-level vision topics such as learned low-level visual representations; depth estimation and optical/scene flow; 6D pose estimation and object tracking. Prerequisites: linear algebra, basic probability and statistics.   

CS 231A: Computer Vision, From 3D Reconstruction to Recognition

Computer Vision

Neural Radiance Fields

2 courses cover this concept

CS 184/284a: Computer Graphics and Imaging

CS 231A: Computer Vision, From 3D Reconstruction to Recognition