Introducing 3D Data Processing
In this chapter, we are going to discuss some basic concepts that are very fundamental to 3D deep learning and that will be used frequently in later chapters. We will begin by learning about the most frequently used 3D data formats, as well as the many ways that we are going to manipulate them and convert them to different formats. We will start by setting up our development environment and installing all the necessary software packages, including Anaconda, Python, PyTorch, and PyTorch3D. We will then talk about the most frequently used ways to represent 3D data – for example, point clouds, meshes, and voxels. We will then move on to the 3D data file formats, such as PLY and OBJ files. We will then discuss 3D coordination systems. Finally, we will discuss camera models, which are mostly related to how 3D data is mapped to 2D images.
After reading this chapter, you will be able to debug 3D deep learning algorithms easily by inspecting output data files. With a solid understanding of coordination systems and camera models, you will be ready to build on that knowledge and learn about more advanced 3D deep learning topics.
In this chapter, we’re going to cover the following main topics:
- Setting up a development environment and installing Anaconda, PyTorch, and PyTorch3D
- 3D data representation
- 3D data formats – PLY and OBJ files
- 3D coordination systems and conversion between them
- Camera models – perspective and orthographic cameras
