3D scanning is the process of analyzing a real-world object or environment to collect three-dimensional data of its shape and possibly its appearance, such as color. The collected data can then be used to construct digital 3D models. A 3D scanner can be based on many different technologies, each with its own limitations, advantages, and costs. Many limitations in the kind of objects that can be digitized are still present. For example, optical technology may encounter difficulties with dark, shiny, reflective, or transparent objects, while industrial computed tomography scanning, structured-light 3D scanners, LiDAR, and time-of-flight 3D scanners can be used to construct digital 3D models without destructive testing.
Collected 3D data is useful for a wide variety of applications. These devices are used extensively by the entertainment industry in the production of movies and video games, including virtual reality. Other common applications of this technology include augmented reality, motion capture, gesture recognition, robotic mapping, industrial design, orthotics and prosthetics, reverse engineering and prototyping, quality control and inspection, and the digitization of cultural artifacts.
Functionality
The purpose of a 3D scanner is usually to create a 3D model. This 3D model consists of a polygon mesh or point cloud of geometric samples on the surface of the subject. These points can then be used to extrapolate the shape of the subject, a process called reconstruction. If color information is collected at each point, then the colors or textures on the surface of the subject can also be determined.
3D scanners share several traits with cameras. Like most cameras, they have a cone-like field of view, and like cameras, they can only collect information about surfaces that are not obscured. While a camera collects color information about surfaces within its field of view, a 3D scanner collects distance information about surfaces within its field of view. The "picture" produced by a 3D scanner describes the distance to a surface at each point in the picture, allowing the three-dimensional position of each point to be identified.
In some situations, a single scan will not produce a complete model of the subject. Multiple scans from different directions are usually helpful to obtain information about all sides of the subject. These scans have to be brought into a common reference system, a process usually called alignment or registration, and then merged to create a complete 3D model. This whole process, going from the single range map to the whole model, is usually known as the 3D scanning pipeline.
Contact Technologies
Contact 3D scanners work by physically probing the part and recording the position of the sensor as the probe moves around the part. There are two main types of contact 3D scanners: coordinate measuring machines (CMMs) and articulated arms.
Coordinate measuring machines traditionally have three perpendicular moving axes with a touch probe mounted on the Z axis. As the touch probe moves around the part, sensors on each axis record the position to generate XYZ coordinates. Modern CMMs are five-axis systems, with the two extra axes provided by pivoting sensor heads. CMMs are the most accurate form of 3D measurement, achieving micron precision. The greatest advantage of a CMM after accuracy is that it can be run in autonomous (CNC) mode or as a manual probing system. The disadvantages of CMMs are their upfront cost and the technical knowledge required to operate them.
Articulated arms generally have multiple segments with polar sensors on each joint. As the articulated arm moves around the part, sensors record their position, and the location of the end of the arm is calculated using complex math and the wrist rotation angle and hinge angle of each joint. While not usually as accurate as CMMs, articulated arms still achieve high accuracy and are cheaper and slightly easier to use. They do not usually have CNC options. Both modern CMMs and articulated arms can also be fitted with non-contact laser scanners instead of touch probes.
Non-Contact Active Technologies
Active scanners emit some kind of radiation or light and detect its reflection or radiation passing through the object in order to probe an object or environment. Possible types of emissions used include light, ultrasound, or X-ray.
Time-of-Flight
The time-of-flight 3D laser scanner is an active scanner that uses laser light to probe the subject. At the heart of this type of scanner is a time-of-flight laser range finder, which finds the distance of a surface by timing the round-trip time of a pulse of light. A laser emits a pulse of light, and the amount of time before the reflected light is seen by a detector is measured. Since the speed of light c is known, the round-trip time determines the travel distance of the light, which is twice the distance between the scanner and the surface. If t is the round-trip time, then distance is equal to câ‹…t/2. The accuracy of a time-of-flight 3D laser scanner depends on how precisely the time t can be measured: 3.3 picoseconds (approximately) is the time taken for light to travel 1 millimeter.
The laser range finder only detects the distance of one point in its direction of view. Thus, the scanner scans its entire field of view one point at a time by changing the range finder's direction of view to scan different points. The view direction can be changed either by rotating the range finder itself or by using a system of rotating mirrors. The latter method is commonly used because mirrors are much lighter and can thus be rotated much faster and with greater accuracy. Typical time-of-flight 3D laser scanners can measure the distance of 10,000 to 100,000 points every second. Time-of-flight devices are also available in a 2D configuration, referred to as a time-of-flight camera.
Triangulation
Triangulation-based 3D laser scanners are also active scanners that use laser light to probe the environment. Unlike time-of-flight scanners, the triangulation laser shines a laser on the subject and exploits a camera to look for the location of the laser dot. Depending on how far away the laser strikes a surface, the laser dot appears at different places in the camera's field of view. This technique is called triangulation because the laser dot, the camera, and the laser emitter form a triangle. The length of one side of the triangle (the distance between the camera and the laser emitter) is known, and the angle of the laser emitter and the angle of the camera are known, so the distance to the subject can be calculated. Triangulation scanners are typically more accurate than time-of-flight scanners but have a shorter range.
Structured Light
Structured-light 3D scanners project a pattern of light (such as grids or stripes) onto the subject and use cameras to measure the deformation of the pattern. The pattern distortion is analyzed to calculate the surface shape. This method can capture a large area in a single shot and is often used for scanning objects at close range, such as in industrial design and cultural artifact digitization. Structured-light scanners can also handle some challenging surfaces better than simple laser triangulation, though they still may struggle with highly reflective or transparent materials.
LiDAR and Time-of-Flight Cameras
LiDAR (Light Detection and Ranging) is a remote sensing method that uses laser pulses to measure distances to the Earth's surface or other objects. It is widely used in applications such as autonomous vehicles, robotic mapping, and topographic surveying. Time-of-flight cameras are a 2D array version of time-of-flight scanners, capturing depth information for every pixel in a scene simultaneously, which is useful for real-time applications like gesture recognition and augmented reality.
Non-Contact Passive Technologies
Passive scanners do not emit any radiation but instead rely on detecting reflected ambient radiation. Most passive scanners detect visible light, which is readily available. Passive techniques include photogrammetry, which uses multiple photographs taken from different angles to reconstruct a 3D model through computer vision algorithms. This approach is often used in cultural heritage preservation and Machine learning-based reconstruction. Other passive methods include silhouette-based techniques and shape-from-shading, though these are less common in industrial applications.
Applications
The collected 3D data from scanning is useful for a wide variety of applications. The entertainment industry uses 3D scanning extensively in the production of movies and video games, including virtual reality environments. In Artificial intelligence and robotics, 3D scanning supports robotic mapping and navigation, enabling autonomous systems to understand their surroundings. Industrial design relies on scanning for reverse engineering and prototyping, allowing engineers to create digital models of existing parts. In healthcare, scanning is used for orthotics and prosthetics, providing custom-fit devices. Quality control and inspection use scanning to verify that manufactured parts meet specifications. Additionally, the digitization of cultural artifacts allows museums and researchers to preserve and share historical objects in digital form.
Challenges and Limitations
Despite advances, 3D scanning still faces challenges. Optical technologies may have difficulty with dark, shiny, reflective, or transparent objects, as these surfaces can scatter or absorb light unpredictably. Scanning large environments can be time-consuming and require significant data processing. Registration of multiple scans can introduce errors if not performed accurately. The cost of high-precision scanners, such as CMMs, remains a barrier for some users. However, ongoing research in Deep learning and Neural network methods is improving reconstruction accuracy and automating parts of the scanning pipeline.
See Also
- ReCoRD
- Wikipedia
- Generative AI
- Google DeepMind
- Intel
- Qualcomm
- Amazon Web Services
- Microsoft Azure
- Google Cloud
- Oracle Cloud Infrastructure
- Xerox PARC
- MIT CSAIL
- Stanford AI Lab
- Carnegie Mellon University
- BAIR (Berkeley AI Research)
- University of Oxford
- Waymo
- Tesla
- Intuitive Surgical
- TomTom