3D graphics – Definition and meaning
What is 3D graphics? Learn all about 3D graphics, from how they work and how they are used in data analysis to specific application scenarios with AI.
Basics and meaning of 3D graphics
3D graphics describe digital representations of objects that are visualised in three dimensions. In contrast to classic two-dimensional illustrations, they convey depth and spatial relationships, allowing even complex relationships and structures to be visualised clearly. Particularly in disciplines such as data analysis and in the field of artificial intelligence, 3D graphics are an established tool for making information visually appealing and interactively accessible.
How do 3D graphics work?
The creation of 3D graphics involves several technical steps that build on each other:
- Modelling: First, basic geometric shapes, known as meshes, are created by arranging points (vertex), edges and surfaces. Alternative methods such as volume data or NURBS are also used depending on the application.
- Texturing and material assignment: Surfaces are then designed. Material properties such as colour, light reflection and transparency are defined in order to give the objects a realistic appearance.
- Rendering: At the end of this process, specialised rendering engines generate realistic images from the mathematical models. These calculate lighting situations, shadows and viewing angles so that the spatial effect on the screen appears convincing.
Numerous software solutions are available for these steps, such as Blender or Autodesk Maya. In the field of web development, developers like to use libraries such as three.js to display 3D content directly in the browser. Up-to-date hardware is also crucial, especially powerful graphics processors (GPUs) that enable calculations in real time.
Practical examples and application scenarios
The possible applications of 3D graphics extend across many sectors. Selected examples illustrate the range:
- Data analysis: 3D representations can be used to visualise correlations in multivariate analysis that would hardly be noticeable in two-dimensional charts. In cluster analysis, for example, data points can be positioned in three-dimensional space in order to quickly recognise groupings.
- Machine learning: Complex networks or data sets with numerous variables can be visualised in 3D scatter plots. This helps, for example, to track the training progress of neural networks and identify anomalies.
- Medicine and science: Radiology uses 3D volume models to visualise organs and tissue structures in detail. Three-dimensional models of proteins are also essential for understanding biological processes in molecular biology.
- Simulations and AI training: Artificial intelligence can train walking behaviour, object recognition or decisions in simulated 3D environments without the need for physical sensors.
- Business intelligence: Many companies rely on interactive dashboards that use 3D charts to visualise large volumes of data. For example, three-dimensional bar charts on world maps provide insights into international sales distributions.
When selecting the right technology solution, it is advisable to pay attention to interfaces to existing data sources and suitable export formats. Python users can achieve convincing visualisations with Plotly or Mayavi, for example, while three.js has proven itself for browser-based 3D models. Those who want to set up AI training environments successfully use simulators such as OpenAI Gym or Unity ML-Agents, which simulate real situations in three-dimensional scenarios.
Advantages and challenges of 3D graphics
Advantages:
- Realistic presentation: Complex content is presented in a comprehensible and visually appealing way.
- Interactive analysis: Users can rotate and zoom data as required and select different perspectives.
- Wide range of applications: Whether for simulations, presentations or research - 3D environments open up new ways of conveying information.
Challenges:
- Technical requirements: The creation and processing of 3D graphics requires specialised software and powerful hardware.
- Familiarisation: Basic knowledge of mathematics and graphics programming is required for a successful start.
- Performance requirements: Large models or demanding renderings can result in longer loading times or performance losses.
Overall, 3D graphics offer considerable added value for data-orientated industries by clearly presenting information and making connections visible. If you consider the objective, area of application and technical possibilities, you can make efficient use of the full potential of three-dimensional visualisations.
Frequently asked questions
3D graphics are digital representations that visualise objects in three dimensions and thus convey depth and spatial relationships. They are used in various areas such as data analysis, where they visualise complex data structures, and in medicine to depict organs in detail. 3D graphics are also indispensable in the entertainment industry and in architecture to create realistic simulations and designs.
3D graphics are created in several steps, starting with modelling, in which geometric shapes are created by arranging points, edges and surfaces. This is followed by texturing, in which material properties such as colour and light reflection are defined. The final step is rendering, in which specialised software creates realistic images by taking lighting situations and shadows into account in order to optimise the spatial effect on the screen.
in data analysis, 3D graphics offer the advantage of visualising complex relationships and correlations that often remain unnoticed in two-dimensional diagrams. The ability to position data points in three-dimensional space means that patterns and groupings can be recognised more quickly. This is particularly valuable in multivariate analyses and cluster analyses, where the visual representation is crucial for interpreting the data.
In the field of artificial intelligence, 3D graphics are used to visualise complex data sets and neural networks. They enable AI models to be trained in simulated environments, where walking behaviour and object recognition can be practised, for example. This allows AI systems to run through realistic scenarios without the need for physical sensors, which increases the efficiency and safety of the training process.
The use of 3D graphics brings with it a number of challenges, including the need for powerful hardware, especially graphics processing units (GPUs), to enable real-time rendering. In addition, the creation of 3D graphics requires expertise in modelling and texturing, which can make access difficult for beginners. The complexity of the software solutions can also be a hurdle, especially for users who are not familiar with the technologies.
Various software solutions are available for creating 3D graphics. Popular programs include Blender and Autodesk Maya, which offer comprehensive functions for modelling, texturing and rendering. For web development, developers often use libraries such as three.js to display 3D content directly in the browser. The choice of software depends on the specific requirements of the project and the existing technical knowledge.