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 (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):

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 SelfCalibration  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 14.314.4 of [MKSS]  
October 15th  (A): Optimization for SLAM  
October 17th  (A): MultiView 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 (Interiorpoint method)  Chapter 14 of [NW]  
October 29th  (T): Proximal Gradient Methods  Proximal Gradient Method  
October 31th  (A): 3D Representation I (Pointcloud and Implicit)  Chapter 12 of [GP]  Homework 3 due. Homework 4 out. 
November 5th  (A): 3D Representation II (Triangular Mesh)  Chapter 12 and Chapter 7 of [BKPAL]  
November 7th  (A): 3D Representation III (Parametric)  
November 12th  (A): RGBDBased 3D Reconstruction (DataDriven)  
November 14th  (A): RGBDBased 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, ADMMSDP  
December 5th  (T): Map Synchronization III  Homework 5 due.  
December 10th  Final Project Presentations  Final project report due. 