The University of Texas at Austin

Computer Science 395T


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 graduatelevel 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 (50%), the Midterm (20%), and the final project (30%). Several final projects are expected to become conference/journal publications.
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):

Date  Topics  Reading  Notes 
August 28th (W)  (A): Introduction  
September 4th (W)  (T):Math Review (Linear Algebra, Rotation, Quaternion)  Rotation Quaternion  Homework 1 
September 9th (M)  (T): Fundamentals of Unconstrained Optimization  Chapter 2 of [NW]  
September 11th (W)  (T): Fundamentals of Constrained Optimization  Chapter 12 of [NW]  
September 16th (M)  (A): Image Formation  Chapter 3 of [MKSS]  
September 18th (W)  (A): Image Primitives and Correspondence  Chapter 4 of [MKSS]  
September 23th (M)  (A): Reconstruction from Two Calibrated Views  Chapter 5 of [MKSS]  
September 25th (W)  (A): Camera Calibration and SelfCalibration  Chapter 6 of [MKSS]  
September 30th (M)  (A): Introduction to Multiple View Reconstruction  Chapter 7 of [MKSS]  Homework 1 due. Homework 2 out. 
October 2th (W)  (T): Line Search Techniques  Chapter 3 of [NW]  
October 7th (M)  (T): Trust Region Methods  Chapter 4 of [NW]  
October 9th (W)  (A): Image Matching and Bundle Adjustment  Chapter 14.314.4 of [MKSS]  
October 14th (M)  (A): Optimization for SLAM  
October 16th (W)  (A): MultiView Stereo  Chapter 14.5 of [MKSS]  Homework 2 due. Homework 3 out. 
October 21th (M)  (T): LargeScale Optimization (Proximal Gradient)  Proximal Gradient Method  Sample Midterm is out. 
October 23th (W)  (T): LargeScale Optimization (ADMM)  
October 28th (M)  3D Representation I (PointCloud)  
October 30th (W)  Midterm  Homework 3 due. Homework 4 out.  
November 4th (M)  (A): 3D Representation II (Implicit)  
November 6th (W)  (A): 3D Representation III (Parametric)  
November 11th (M)  (A): 3D Representation IV (Triangular Mesh)  
November 13th (W)  (A): 3D Representation V (PartBased and SceneGraph)  
November 18th (M)  (A): Hybrid 3D Representation  Homework 4 due. Homework 5 out.  
November 20th (W)  (A): 3D Deep Learning I (Understanding)  
November 25th (M)  (A): 3D Deep Learning II (Understanding)  
December 2th (M)  (T): 3D Deep Learning III (Synthesis)  
December 4th (W)  (T): 3D Deep Learning IV (Synthesis)  Homework 5 due.  
December 9th (M)  Final Project Presentations  Final project report due. 