3D Data Science with Python

Foreword

The world around us is incredibly diverse and, most importantly, three-dimensional. Everything has a position and a meaning. To better understand our environment and answer current questions, we need accurate representations of the real world to be represented in the virtual world. These representations are often referred to as digital twins. While digital twins usually encompass more than just three-dimensional images, they include as much additional data and information as possible. However, one thing is common in almost all digital twins: they require a foundational dataset that is three-dimensional, or often even four-dimensional, when we include the temporal component. Hence, 3D data is transforming our world in many areas, whether in geosciences, self-driving cars, or medicine.

In 3D Data Science with Python, Florent demonstrates why this data is so valuable and how it can be effectively utilized and leveraged. There are numerous ways to generate 3D data, such as photogrammetric methods to generate point clouds from images, laser scanners that “scan” the environment, or MRI machines that create an image of the body, to name a few. What all these methods have in common is the fact that, although they generate highly accurate data, this data lacks semantic information. The real value is created through the processing and analysis of the data. To achieve value, nowadays one cannot avoid using AI methods and their implementation in Python.

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