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Special Topics on Embedded Systems: Neural Rendering

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Special Topics on Embedded Systems: Neural Rendering

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Neural Fields in Visual Computing and Beyond

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Neural Fields in Visual Computing and Beyond

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Computer Graphics Forum - 2022 - Xie - Neural Fields in Visual Computing and Beyond

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 Ma, Xudong, et al. 3D Deep Learning with Python: Design and Develop Your Computer Vision Model with 3D Data Using PyTorch3D and More. Packt Publishing Ltd, 2022.

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 Ma, Xudong, et al. 3D Deep Learning with Python: Design and Develop Your Computer Vision Model with 3D Data Using PyTorch3D and More. Packt Publishing Ltd, 2022.

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Part 1 3D Data Processing Basics
1 Introducing 3D Data Processing
1.1 Understanding Mesh Representation
1.2 Understanding Voxel Representation
1.3 Coding for Camera Models and Coordination Systems

2 Introducing 3D Computer Vision and Geometry
2.1 Exploring the Basic Concepts of Rendering, Rasterization, and Shading
2.3 Understanding Transformations and Rotations

3 3D Deep Learning Using PyTorch3D
3.1 Fitting Meshes to Point Clouds
3.2 Loss Functions for Regularization
3.3 Implementing the Mesh Fitting with PyTorch3D

4 Learning Object Pose Detection and Tracking by Differentiable Rendering
4.1 How to Make Rendering Differentiable
4.2 The Object Pose Estimation Problem

5 Understanding Differentiable Volumetric Rendering
5.1 Overview of Volumetric Rendering
5.2 Understanding Ray Sampling
5.3 Differentiable Volumetric Rendering

6 Exploring Neural Radiance Fields (NeRF)
6.1 Understanding NeRF
6.2 What Is a Radiance Field?
6.3 Representing Radiance Fields with NN
6.4 Training a NeRF Model
6.5 Projecting Rays into the Scene
6.6 Accumulating the Color of a Ray

Part 3 State-of-the-Art 3D Deep Learning Using PyTorch3D
7 Exploring Controllable Neural Feature Fields
7.1 Compositional 3d-Aware Image Synthesis
7.2 Generating Feature Fields
7.3 Controllable Scene Generation
7.4 Controllable Car Generation
7.5 Controllable Face Generation
7.6 Training the GIRAFFE Model
7.7 Frechet Inception Distance

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3D Deep Learning with Python_ Design and develop your computer vision model with 3D data using PyTorch3D and more

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A PyTorch Library and Engine for Neural Fields Research

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A PyTorch Library and Engine for Neural Fields Research

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https://github.com/NVIDIAGameWorks/kaolin-wisp.git
https://github.com/NVIDIAGameWorks/kaolin-wisp.git

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GitHub - NVIDIAGameWorks/kaolin-wisp: NVIDIA Kaolin Wisp is a PyTorch library powered by NVIDIA Kaolin Core to work with neural fields (including NeRFs, NGLOD, instant-ngp and VQAD).

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NVIDIA Kaolin Wisp is a PyTorch library powered by NVIDIA Kaolin Core to work with neural fields (including NeRFs, NGLOD, instant-ngp and VQAD). - GitHub - NVIDIAGameWorks/kaolin-wisp: NVIDIA Kaolin Wisp is a PyTorch library powered by NVIDIA Kaolin Core to work with neural fields (including NeRFs, NGLOD, instant-ngp and VQAD).

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Safe and Uncertainty-Aware Learning: How Can We Predict and React to Rare but Potentially Disastrous Events?

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Safe and Uncertainty-Aware Learning: How Can We Predict and React to Rare but Potentially Disastrous Events?

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Theoretical foundations of risk-sensitive decision-making and learning. Deployment of safety-critical systems in uncertain environments requires predicting and reacting to rare but potentially disastrous event. The focus is to devise risk-sensitive algorithm for various types of real world scenarios. This includes algorithms for risk-sensitive planning, for inferring the profile of risk-sensitive expert (e.g., inverse reinforcement learning, imitation learning). for interactive decision making for self-driving cars (e.g., for traffic weaving scenarios), for safe transfer of control policies from simulation environment to the real world (e.g., autonomous driving in varying weather conditions), and new techniques to merge formal methods with stochastic optimal control and deep learning for high-confidence implementation on safety-critical systems. (https://stanfordasl.github.io//projects/SafeUncertLearning/)

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  Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach, Global Edition. 2016.

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  Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach, Global Edition. 2016.

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Part 1 Artificial Intelligence
1 Introduction
2 Intelligent Agents

Part 2 Problem-Solving
3 Solving Problems by Searching
4 Search in Complex Environments
5 Constraint Satisfaction Problems
6 Adversarial Search and Games

Part 3 Knowledge, Reasoning, and Planning
7 Logical Agents
8 First-Order Logic
9 Inference in First-Order Logic
10 Knowledge Representation
11 Automated Planning

Part 4 Uncertain Knowledge and Reasoning
12 Quantifying Uncertainty
13 Probabilistic Reasoning
14 Probabilistic Reasoning over Time
15 Making Simple Decisions
16 Making Complex Decisions
17 Multi-Agent Decision Making
18 Probabilistic Programming

Part 5 Machine Learning
19 Learning from Examples
20 Knowledge in Learning
21 Learning Probabilistic Models
22 Deep Learning
23 Reinforcement Learning

Part 6 Communicating, Perceiving, and Acting
24 Natural Language Processing
25 Deep Learning for Natural Language Processing
26 Robotics
27 Computer Vision
28 Philosophy, Ethics, and Safety of AI
29 The Future of AI

Appendix A. Mathematical Background

A.1 Complexity Analysis
A.2 Vectors, Matrices, and Linear Algebra
A.3 Probability Distributions

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Artificial Intelligence A Modern Approach, Global Edition.pdf

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M3309.002700

1 How to Solve Linear Equations
2 Theory of Linear Equations
3 Cholesky, LU, QR Decomposition
4 Least Square, Least Norm Problems
5 Eigenvalues and Eigenvectors
6 Positive Definite Matrices
7 Pseudo-Inverses and Generalized Inverses
8 Singular Value Decomposition
9 Gradient-Based Optimization
10 Newton Method
11 Conjugate Gradient Method
12 Linear Programming
13 Convex Optimization

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Mildenhall, Ben, et al. "Nerf: Representing scenes as neural radiance fields for view synthesis." Communications of the ACM 65.1 (2021): 99-106.

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NeRF Representing Scenes as Neural Radiance Fields for View Synthesis

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Martin-Brualla, Ricardo, et al. "Nerf in the wild: Neural radiance fields for unconstrained photo collections." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.

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NeRF in the Wild Neural Radiance Fields for Unconstrained Photo Collections

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Kendall, Alex, and Yarin Gal. "What uncertainties do we need in bayesian deep learning for computer vision?." Advances in neural information processing systems 30 (2017).

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What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision

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Gustafsson, Fredrik K., Martin Danelljan, and Thomas B. Schon. "Evaluating scalable bayesian deep learning methods for robust computer vision." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 2020.

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Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision

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Abdar, Moloud, et al. "A review of uncertainty quantification in deep learning: Techniques, applications and challenges." Information Fusion 76 (2021): 243-297.

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A Review of Uncertainty Quantification in Deep Learning Techniques Applications Challenges

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Shen, Jianxiong, et al. "Stochastic neural radiance fields: Quantifying uncertainty in implicit 3d representations." 2021 International Conference on 3D Vision (3DV). IEEE, 2021.

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Stochastic Neural Radiance Fields Quantifying Uncertainty in Implicit 3D Representations

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Jospin, Laurent Valentin, et al. "Hands-on Bayesian neural networks—A tutorial for deep learning users." IEEE Computational Intelligence Magazine 17.2 (2022): 29-48.

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Hands-On Bayesian Neural Networks-A Tutorial for Deep Learning Users

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Tang, Xiaolin, Kai Yang, Hong Wang, Jiahang Wu, Yechen Qin, Wenhao Yu, and Dongpu Cao. "Prediction-Uncertainty-Aware Decision-Making for Autonomous Vehicles." IEEE Transactions on Intelligent Vehicles 7.4 (2022): 849-62. Web.

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Yang, Guo-Wei, et al. "Recursive-NeRF: An efficient and dynamically growing NeRF." IEEE Transactions on Visualization and Computer Graphics (2022).

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Recursive-NeRF An Efficient and Dynamically Growing NeRF

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Pan, Xuran, et al. "ActiveNeRF: Learning Where to See with Uncertainty Estimation." Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIII. Cham: Springer Nature Switzerland, 2022.

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ActiveNeRF Learning where to See with Uncertainty

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Sünderhauf, Niko, Jad Abou-Chakra, and Dimity Miller. "Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields." arXiv preprint arXiv:2209.08718 (2022).

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Density-Aware NeRF Ensembles Quantifying Predictive Uncertainty in Neural Radiance Fields

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Hoffman, Matthew D., et al. "ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images." arXiv preprint arXiv:2210.17415 (2022).

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ProbNeRF Uncertainty-Aware Inference of 3D Shapes from 2D Images

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  Gentle, James E. Matrix Algebra: Theory, Computations, and Applications in Statistics. Springer Science & Business Media, 2007.

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  Gentle, James E. Matrix Algebra: Theory, Computations, and Applications in Statistics. Springer Science & Business Media, 2007.

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Part 1 Linear Algebra
1. Basic Vector/Matrix Structure and Notation
2. Vector and Vector Spaces
3. Basic Properties of Matrices
4. Vector/Matrix Derivatives and Integrals
5. Matrix Transformation and Factorizations
6. Solutions of Linear Systems
7. Evaluation of Eigenvalues and Eigenvectors

Part 2 Applications in Data Analysis
8 Special Matrices and Operations Useful in Modeling and Data Analysis
9 Selected Applications in Statistics

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Matrix Algebra: Theory, Computations, and Applications in Statistics.pdf

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Haykin, Simon S. Neural Networks and Learning Machines. Prentice Hall, 2009.

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Haykin, Simon S. Neural Networks and Learning Machines. Prentice Hall, 2009.

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1 RosenBlatt's Perceptron
2 Model Building through Regression
3 The Least-Mean-Square Algorithm
4 Multilayer Perceptrons
5 Kernel Methods and Radial-Basis Function Networks
6 Support Vector Machines
7 Regularization Theory
8 Principal-Component Analysis
9 Self-Organizing Maps
10 Information-Theoretic Learning Models
11 Stochastic Methods Rooted in Statistical Mechanics
12 Dynamic Programming
13 Neurodynamics
14 Bayesian Filtering for State Estimation of Dynamic Systems
15 Dynamically Driven Recurrent Networks

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Neural networks  a comprehensive foundation(Cover Removed).pdf

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Thurner, Stefan, et al. Introduction to the Theory of Complex Systems. Oxford University Press, 2019. 

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Thurner, Stefan, et al. Introduction to the Theory of Complex Systems. Oxford University Press, 2019. 

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1 Introduction to Complex Systems
2 Probability and Random Processes
3 Scaling
4 Networks
5 Evolutionary Processes
6 Statistical Mechanics and Information Theory for Complex Systems
7 The Future of the Science of Complex Systems

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Introduction to the Theory of Complex Systems by Stefan Thurner, Rudolf Hanel, Peter Klimek (z-lib.org)

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Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2005.

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Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2005.

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1 Introduction
1.1 A Pictorial Introduction to Bayesian Modelling
1.2 Roadmap

2. Regression
2.1 Weight-Space View
2.2 Function-Space View
2.3 Varying the Hyperparameters
2.4 Decision Theory for Regression
2.5 An Example Application
2.6 Smoothing, Weight Functions and Equivalent Kernels
2.7 Incorporating Explicit Basic Functions

3 Classification
3.1 Classification Problems
3.2 Linear Models for Classification
3.3 Gaussian Process Classification
3.4 The Laplace Approximation for The Binary GP Classifier
3.5 Multi-Class Laplace Approximation
3.6 Expectation Propagation
3.7 Experiments

4 Covariance Functions
4.1 Preliminaries
4.2 Examples of Covariance Functions
4.3 Eigenfunction Analysis of Kernels
4.4 Kernels for non-Vectorial Input

5 Model Selection and Adaptation of Hyerparameters
5.1 The Model Selection Problem
5.2 Bayesian Model Selection
5.3 Cross-Validation
5.4 Model Selection for GP Regression
5.5 Model Selection for GP Classification

Appendix A Mathematical Background
A.1 Joint, Marginal, and Conditional Probability
A.2 Gaussian Identities
A.3 Matrix Identities
A.4 Cholesky Decomposition
A.5 Entropy and Kullback-Leibler Divergence
A.6 Limits
A.7 Measure and Integration
A.8 Fourier Transforms
A.9 Convexity

Appendix B Gaussian Markov Processes
B.1 Fourier Analysis
B.2 Continuous-Time Gaussian Markov Processes
B.3 Discrete-Time Gaussian Markov Processes
B.4 The Relationship Between Discrete-Time and Sampled Continuous-Time GMPs
B.5 Markov Processes in Higher Dimensions

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Gaussian Processes for Machine Learning (Carl Edward Rasmussen etc.) (z-lib.org).pdf

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Orland, Paul. Math for Programmers: 3D Graphics, Machine Learning, and Simulations with Python. Manning Publications, 2021.

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Orland, Paul. Math for Programmers: 3D Graphics, Machine Learning, and Simulations with Python. Manning Publications, 2021.

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Barber, David. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.

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Barber, David. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.

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Bayesian Reasoning and Machine Learning (David Barber) (z-lib.org).pdf

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Hertzmann, Aaron. Introduction to Bayesian Reasoning. Course Notes, 15 Sept. 2004.

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Hertzmann, Aaron. Introduction to Bayesian Reasoning. Course Notes, 15 Sept. 2004.

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https://drive.google.com/file/d/1RMS__cJgsOJihLogESYNRrW9UDkD0fFD/view?usp=share_link

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Hertzmann, Aaron. Introduction to Bayesian Reasoning. Course Notes, 15 Sept. 2004..pdf

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Yu, Alex, et al. "pixelnerf: Neural radiance fields from one or few images." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.

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Yu, Alex, et al. "pixelnerf: Neural radiance fields from one or few images." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.

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pixelNeRF Neural Radiance Fields from One or Few Images

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Park, Keunhong, et al. "Nerfies: Deformable neural radiance fields." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.

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Park, Keunhong, et al. "Nerfies: Deformable neural radiance fields." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.

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Nerfies Deformable Neural Radiance Fields

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Janke, Steven J. Mathematical Structures for Computer Graphics. John Wiley & Sons, 2014.

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Janke, Steven J. Mathematical Structures for Computer Graphics. John Wiley & Sons, 2014.

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1 Basics
1.1 Graphics Pipeline

2 Vector Algebra
3 Vector Geometry
3.1 Homogeneous Coordinates

4 Transformations
4.1 Linear Transformations
4.2 Three Dimensions
4.3 Affine Transformation

5 Orientation
5.1 Cartesian Coordinate Systems
5.2 Cameras
5.3 Polar, Cylindrical, and Spherical Coordinates

6 Polygons and Polyhedra
6.1 Interpolation
6.2 Polygons
6.3 Polyhedra

7 Curves and Surfaces

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Mathematical Structures For Computer Graphics

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Benjamin Fung on mobile device

Canada Research Chair in Data Mining for Cybersecurity

Graduate Program Director - MISt & Graduate Certificates

Professor, School of Information Studies

McGill University, Montreal, Canada

http://dmas.lab.mcgill.ca/fung

(click on Schedule to make an appointment)