3D Data Science with Python

Chapter 4. 3D Data Representation and Structuration

3D data representations, such as point clouds, meshes, parametric models, voxels, and depth maps, offer various methods to describe and handle 3D geometric entities. Knowing their differences and how to manage their uniqueness is essential. It helps us pick the right format for a task, improve workflows, and ensure different applications work well together.

Moreover, with the rise in 3D data volume, we need efficient ways to handle increasingly complex 3D datasets that often appear in different shapes and formats. Indeed, this can cause significant problems when attempting to integrate and establish robust 3D analytical workflows. Consider the time you spend changing formats, handling compatibility problems, and trying to find common ground for your analyses. These are not just minor issues; they affect your productivity and restrict the potential of your advanced work.

A unified way to represent 3D data is essential for opening up new opportunities and overcoming the challenges of different formats. Picture a process where you can easily switch between 3D meshes, point clouds, and volumetric models.

This chapter examines point clouds as a key data link and shows how they can connect different representations. We’ll also cover how to integrate various 3D data representations to establish core data structures to speed up computation (see Figure 4-1).

Figure 4-1. High-level workflow for establishing core data structures

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