The University of Texas at Austin
Computer Science Department

Computer Science 395T
Numerical Optimization for Graphics and AI (3D Vision)

Fall 2018

General Information:

Time: Mondays and Wednesdays 3:30PM-5:00PM
Place: GDC 1406
Instructor: Qixing Huang
Office hour: Fridays 3pm-5pm at GDC 5422.

This course will cover a broad range of topics in the general area of 3D Vision and 3D Geometry Processing, ranging from 1) reconstructing 3D models from images and depth scans, 2) 3D representations (e.g., for neural networks), and 3) analysis and processing of 3D models. An unique characteristics of this course is that we will install the basic theory of numerical optimization throughout. The course is a graduate-level course that combines instruction of basic material, written homeworks , and a final project. The course targets for students who will conduct research in Graphics, Vision, Robotics, and Computational Biology .Grading is based on homeworks (70%) and the final project (30%).

Prereqs: The course assumes a good knowledge of linear algebra and probability. Please talk to me or email me if you are unsure if the course is a good match for your background.

Textbooks (Not Required but Recommended):


[NW]: Numerical Optimization


[MKSS]: An Invitation to 3D Vision


[BKPAL]:Polygon Mesh Processing


[GP]: Point-Based Graphics

Schedule (A: Application, T: Theory):

Date Topics Reading Notes
August 29th (A): Introduction
September 5th (T): Math Review (Linear Algebra, Rotation, Quaternion) Rotation Quaternion Homework 1
September 10th (T): Fundamentals of Unconstrained Optimization Chapter 2 of [NW]
September 12th (T): Fundamentals of Constrained Optimization Chapter 12 of [NW]
September 17th (A): Image Formation Chapter 3 of [MKSS]
September 19th (A): Image Primitives and Correspondence Chapter 4 of [MKSS]
September 24th (A): Reconstruction from Two Calibrated Views Chapter 5 of [MKSS]
September 26th (A): Camera Calibration and Self-Calibration Chapter 6 of [MKSS]
October 1th (A): Introduction to Multiple View Reconstruction Chapter 7 of [MKSS] Homework 1 due. Homework 2 out.
October 3th (T): Line Search Techniques Chapter 3 of [NW]
October 8th (T): Trust Region Methods Chapter 4 of [NW]
October 10th (A): Image Matching and Bundle Adjustment Chapter 14.3-14.4 of [MKSS]
October 15th (A): Optimization for SLAM
October 17th (A): Multi-View Stereo Chapter 14.5 of [MKSS] Homework 2 due. Homework 3 out.
October 22th (T): Linear Programming (Simplex method) Chapter 13 of [NW]
October 24th (T): Linear Programming (Interior-point method) Chapter 14 of [NW]
October 29th (T): Proximal Gradient Methods Proximal Gradient Method
October 31th (A): 3D Representation I (Pointcloud and Implicit) Chapter 1-2 of [GP] Homework 3 due. Homework 4 out.
November 5th (A): 3D Representation II (Triangular Mesh) Chapter 1-2 and Chapter 7 of [BKPAL]
November 7th (A): 3D Representation III (Parametric)
November 12th (A): RGBD-Based 3D Reconstruction (Data-Driven)
November 14th (A): RGBD-Based 3D Reconstruction (ICP)
November 19th (A): 3D Deep Learning I Homework 4 due. Homework 5 out.
November 26th (A): 3D Deep Learning II
November 28th (A): Map Synchronization I
December 3th (T): Map Synchronization II SDP, ADMM-SDP
December 5th (T): Map Synchronization III Homework 5 due.
December 10th Final Project Presentations Final project report due.

Final Project:

The final project is done in groups of 2-3 students. Each project should have an initial proposal, a final report, and a final poster presentation. The project proposal shall describe four key components of a research project (namely Motivation, Technical Merit, Broader Impact, and Project Plan). The final report should be written as an academic research article. A more detailed instruction will be given later.