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

Cover
Title Page
Copyright and Credits
Contributors
Table of Contents
Preface
PART 1: 3D Data Processing Basics
	Chapter 1: Introducing 3D Data Processing
		Technical requirements
		Setting up a development environment
		3D data representation
			Understanding point cloud representation
			Understanding mesh representation
			Understanding voxel representation
		3D data file format – Ply files
		3D data file format – OBJ files
		Understanding 3D coordination systems
		Understanding camera models
		Coding for camera models and coordination systems
		Summary
	Chapter 2: Introducing 3D Computer Vision and Geometry
		Technical requirements
		Exploring the basic concepts of rendering, rasterization, and shading
			Understanding barycentric coordinates
			Light source models
			Understanding the Lambertian shading model
			Understanding the Phong lighting model
		Coding exercises for 3D rendering
		Using PyTorch3D heterogeneous batches and PyTorch optimizers
			A coding exercise for a heterogeneous mini-batch
		Understanding transformations and rotations
			A coding exercise for transformation and rotation
		Summary
PART 2: 3D Deep Learning Using PyTorch3D
	Chapter 3: Fitting Deformable Mesh Models to Raw Point Clouds
		Technical requirements
		Fitting meshes to point clouds – the problem
		Formulating a deformable mesh fitting problem into an optimization problem
		Loss functions for regularization
			Mesh Laplacian smoothing loss
			Mesh normal consistency loss
			Mesh edge loss
		Implementing the mesh fitting with PyTorch3D
			The experiment of not using any regularization loss functions
			The experiment of using only the mesh edge loss
		Summary
	Chapter 4: Learning Object Pose Detection and Tracking by Differentiable Rendering
		Technical requirements
		Why we want to have differentiable rendering
		How to make rendering differentiable
			What problems can be solved by using differentiable rendering
		The object pose estimation problem
		How it is coded
			An example of object pose estimation for both silhouette fitting and texture fitting
		Summary
	Chapter 5: Understanding Differentiable Volumetric Rendering
		Technical requirements
		Overview of volumetric rendering
		Understanding ray sampling
		Using volume sampling
		Exploring the ray marcher
		Differentiable volumetric rendering
			Reconstructing 3D models from multi-view images
		Summary
	Chapter 6: Exploring Neural Radiance Fields (NeRF)
		Technical requirements
		Understanding NeRF
			What is a radiance field?
			Representing radiance fields with neural networks
		Training a NeRF model
		Understanding the NeRF model architecture
		Understanding volume rendering with radiance fields
			Projecting rays into the scene
			Accumulating the color of a ray
		Summary
PART 3: State-of-the-art 3D Deep Learning Using PyTorch3D
	Chapter 7: Exploring Controllable Neural Feature Fields
		Technical requirements
		Understanding GAN-based image synthesis
		Introducing compositional 3D-aware image synthesis
		Generating feature fields
		Mapping feature fields to images
		Exploring controllable scene generation
			Exploring controllable car generation
			Exploring controllable face generation
		Training the GIRAFFE model
			Frechet Inception Distance
			Training the model
		Summary
	Chapter 8: Modeling the Human Body in 3D
		Technical requirements
		Formulating the 3D modeling problem
			Defining a good representation
		Understanding the Linear Blend Skinning technique
		Understanding the SMPL model
			Defining the SMPL model
		Using the SMPL model
		Estimating 3D human pose and shape using SMPLify
			Defining the optimization objective function
		Exploring SMPLify
			Running the code
			Exploring the code
		Summary
	Chapter 9: Performing End-to-End View Synthesis with SynSin
		Technical requirements
		Overview of view synthesis
		SynSin network architecture
			Spatial feature and depth networks
			Neural point cloud renderer
			Refinement module and discriminator
		Hands-on model training and testing
		Summary
	Chapter 10: Mesh R-CNN
		Technical requirements
		Overview of meshes and voxels
		Mesh R-CNN architecture
			Graph convolutions
			Mesh predictor
		Demo of Mesh R-CNN with PyTorch
			Demo
		Summary
Index
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