In the ever-evolving landscape of data, one category that has gained significant attention is 3D data. Ever since Apple first unveiled LiDAR - a 3D laser scanning technology - on the iPhone and iPad Pro in 2020, 3D data has gradually made its way into the mainstream. From augmented reality applications to 3D mapping, 3D data plays a pivotal role in shaping the future. However, this data is not without its challenges.

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What is 3D Data?

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Very simply, 3D data refers to any information that represents objects or scenes in three-dimensional space. Unlike traditional 2D data, such as photographs and videos which are confined to the x and y dimensions (length and width), 3D data adds an extra dimension of depth (z). It allows us to represent the spatial characteristics of objects and environments, enabling a more immersive and realistic understanding of the world around us.

How Do You Acquire 3D Data?

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There are several ways to acquire 3D data:

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1. LiDAR (Light Detection and Ranging): LiDAR technology uses lasers to measure and map physical surfaces. By scanning the environment from different angles, LiDAR generates accurate and detailed 3D “point clouds” - a collection of millions of points created by laser beams. Initially developed by the U.S. military to scan for missiles during the Cold War, LiDAR has gone from an expensive, niche technology to one that is now standard on iPhones. To learn more about LiDAR technology, check out Inertial Labs, Leica, Exyn Technologies, and NavVis VLX.
2. Photogrammetry: Whereas LiDAR captures 3D data from a continuous laser scan, photogrammetry involves capturing hundreds of 2D photos of an object from different angles and then using specialized software to stitch together a 3D model. Some of the most common photogrammetry software include Pix4D, Matterport, and Agisoft Metashape. And, like LiDAR, photogrammetry can be captured from a variety of platforms, including drones, tripods, or just a good old-fashioned DSLR camera.
3. NeRF (Neural Radiance Fields): NeRF is a new state-of-the-art machine learning technique that uses data to generate highly realistic 3D scenes from 2D images. Unlike photogrammetry, NeRF does not require hundreds of photos to create a 3D object. Instead, NeRF uses artificial intelligence to represent 3D scenes from just a handful of 2D images taken from various viewpoints. Although this technology is still developing, NeRF-based mobile apps such as LumaAI have demonstrated promising results.

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What are the Applications of 3D Data?

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1. Architecture, Engineering, and Construction (AEC): The AEC industry has long relied on antiquated 2D data such as building blueprints and drawings. With 3D data, however, architects and engineers now have access to fast and reliable 3D virtual models known as “digital twins.” Digital twins help architects and property managers significantly improve the time required for design, construction, and renovation work.
2. 3D Printing: A 3D printer requires 3D data to print using “additive” or “subtractive” manufacturing processes. The most common file formats used in 3D printing are STL and OBJ which are known as 3D meshes.
3. Computer Vision: One of the most exciting artificial intelligence fields, computer vision uses image data to teach computers how to interpret and understand the real world. Computer vision is now capable of advanced tasks such as image recognition, object detection, and 3D navigation, leading to industry breakthroughs in autonomous vehicles, medical imaging, and robotics.
4. Urban planning and Geospatial Analysis: 3D data assists smart city and urban planners in creating detailed city models and analyzing various parameters such as population density, energy consumption, and floods using Digital Elevation Models.

3D data is slowly permeating into many existing industries and creating entirely new ones!

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Challenges in Working with 3D Data

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Despite its immense potential, 3D data comes with its share of challenges:

1. Data Size and Storage: 3D data tends to be significantly larger than traditional 2D data, making storage, processing, and data sharing more demanding.
2. Data Processing and Visualization: Analyzing 3D data requires specialized software and powerful machines, increasing the complexity for cloud processing and 3D visualization.
3. Interoperability: There are literally hundreds of 3D file formats that are used across many industries including gaming, printing, manufacturing, and engineering. Standardization and interoperability between different 3D data formats remain challenging, preventing the seamless data exchange between different types of 3D files.
4. Noise and Artifacts: 3D scans may contain noise and artifacts that can negatively impact the accuracy of the scene or object. Moving objects, for example, can generate erroneous data in the point cloud and affect 3D mapping algorithms.

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Conclusion

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As 3D technologies continue to evolve, 3D data will soon hit the mainstream, pushing the boundaries of possibility in multiple industries. Many people are already exploring 3D technologies and capturing real-world environments with incredible 3D precision. Stitch3D is committed to making 3D data accessible for everyone, helping to take human experiences and understanding into the third dimension.