Research Papers:

• Multi-stage SFM: A Coarse-to-Fine Approach for 3D Reconstruction
• Metrics for 3D Rotation: Comparison and Analysis
• Analyzing 3D Objects in Cluttered Images - NRSFM applied on Cars
• NRSFM Tutorial
• Shape and motion from image streams under orthography: A factorization method - Seminal Work on Factorization based Approaches for Structure Recovery

Books:

• [Computer Vision: Models, Learning, and Inference] (Download)

• [Computer Vision: Models, Learning, and Inference (Algorithms Booklet)] (Download)

• [Computer Vision: Models, Learning, and Inference (Answers Booklet for Students)] (Download)

Lecture Notes:

• [Gaussian Mixtures and the EM Algorithm] (Download)
• [EM Clarification] (Download)
• [EM Derivation, Proof that EM works] (Download)
• [GMM and K-Means] (Download)
• [GMM and EM Intro] (Download)
• [Mixture of Gaussians Lecture] (Download)
• [Mixture of Gaussians Tutorial - Reynolds] (Download)
• [Mixture Models and the EM Algorithm - C. Bishop] (Download)
• [Estimating Gaussian Mixture Densities with EM: A Tutorial - Tomasi] (Download)
• [A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for GMMs and HMMs] (Download)
• [Tutorial on Mixture Models] (Download)
• [Introduction to Graphical Models, Belief Propagation] (Download)
• [Mixture of Gaussians Tutorial] (Download)
• [Mixture Models] (Download)
• [A Unifying Review of Linear Gaussian Models] (Download)

Course Material:

• Mathematical Foundations of Computer Graphics and Vision