Tech Focus: 3D Imaging
3D imaging is the process of capturing spatial depth and geometric structure of objects. It enables computer vision devices to perceive and interact with the physical world in three dimensions.
From automated inspection and medical diagnostics to robotics and spatial computing, 3D imaging is becoming commonplace across diverse sectors. Understanding the different 3D imaging techniques is essential for engineers and system integrators working with imaging hardware such as multi-camera setups and high-throughput frame grabbers. This enables the design of accurate, high-performance systems.
This Tech Focus provides an overview of the most widely used 3D imaging methods along with the components necessary to implement each technique.
What Is 3D Imaging?
3D imaging refers to technologies that capture the depth, shape, and volume of real-world objects. Unlike 2D systems that record only intensity and color across a flat plane, 3D systems produce spatially resolved data, typically in the form of point clouds, depth maps, or volumetric datasets.
Depending on your application, whether it’s micron-level inspection in electronics, real-time machine guidance, or medical volume reconstruction, different 3D methods may be appropriate.
3D Imaging Techniques
Stereo vision is one of the most widely adopted 3D imaging techniques, leveraging two or more cameras to mimic human binocular perception. By identifying corresponding points between the captured images and calculating their disparity, a stereo system can triangulate depth and generate dense depth maps. This approach works well in environments with sufficient texture and lighting, and is commonly used in robotic navigation, bin picking, and real-time inspection. For best results, precise camera calibration and synchronization are essential, along with dedicated processing. This processing power can often be implemented on FPGAs or GPUs to handle the computational load of disparity estimation in real time.
Structured light systems project a predefined pattern, such as stripes, dot matrices, or fringe patterns, onto a target scene. The deformation of this pattern, as observed from a separate camera viewpoint, reveals surface topology by analyzing how the structured light distorts over varying depths. This method offers excellent spatial resolution and is particularly useful for detailed scanning tasks, such as in metrology, facial recognition, and 3D modeling.
Time-of-Flight (ToF) imaging calculates depth based on the time it takes for light to travel from an emitter to an object and back to the sensor. This time delay is translated directly into distance measurements for each pixel in the image. ToF systems can operate in real time and are ideal for applications that need fast depth capture, such as gesture recognition, obstacle detection, and augmented reality. However, ToF sensors may struggle with reflective or absorptive materials and often require onboard correction mechanisms for multipath interference, temperature drift, and ambient light noise.
Laser triangulation involves projecting a laser line or point onto a surface and capturing its profile with a camera positioned at a known angle. As either the object or sensor moves, a sequence of profiles is collected and stitched together to form a high-resolution 3D reconstruction. This method excels in precision applications like surface inspection, electronics manufacturing, and dimensional measurement.
Photogrammetry reconstructs 3D geometry from a series of overlapping 2D images taken from different viewpoints. Using algorithms such as structure-from-motion (SfM) and multi-view stereo (MVS), it identifies shared features across images and estimates their position in 3D space. While photogrammetry requires more post-processing than other methods, it can produce highly detailed models using off-the-shelf cameras. It is widely used in geospatial mapping, construction monitoring, and digital archiving.
Volumetric imaging captures internal structure within a three-dimensional space, unlike surface-based techniques. This can be achieved using tomographic methods like computed tomography (CT), where multiple 2D X-ray images are taken around an axis and computationally reconstructed into a volumetric dataset. In the optical domain, techniques such as optical coherence tomography (OCT) use low-coherence interferometry to resolve micro-scale internal features in biological tissue or layered materials. Volumetric imaging systems are typically data-intensive, requiring high-speed cameras and frame grabbers, precise timing mechanisms, and powerful reconstruction pipelines to achieve real-time or high-fidelity output.
System Requirements
Whether the goal is depth mapping, full-volume reconstruction, or real-time 3D scanning, most 3D imaging systems share several core hardware requirements.
For stereo vision, photogrammetry, or volumetric capture, using synchronized multi-camera setups is essential to ensure accurate and consistent 3D reconstruction. Each camera must capture images at the same time and from precise angles to properly align and combine the data. Without proper synchronization, even small timing differences can lead to depth errors or misaligned models. In dynamic scenes where the distance to objects can change, autofocus-zoom cameras provide added flexibility by automatically adjusting focus and field of view. This allows the system to maintain sharp, high-quality images across varying depths, which is especially important in environments with moving subjects or complex geometry.
Frame grabbers deliver benefits to high-performance imaging systems by managing key tasks such as triggering, timestamping, and buffer control. They coordinate when and how images are captured, ensuring that data from multiple cameras or sensors is synchronized and accurately timed. Many frame grabbers also include onboard FPGA processing, which allows for real-time image preprocessing, data compression, or feature extraction before the data reaches the host computer. This offloading greatly reduces system latency and increases overall throughput. Frame grabbers are especially valuable in high-speed, multi-sensor environments where large volumes of data need to be captured and processed with minimal delay.
Controlled lighting is essential in 3D imaging systems such as structured light, laser triangulation, and time-of-flight (ToF) to ensure accurate and consistent depth measurements. These methods rely on projecting light patterns or pulses onto the scene, so the type of light source used plays a critical role. Depending on the surface properties of the objects being scanned – such as reflectivity, texture, and color – different projection technologies may be more effective. Infrared (IR) sources, laser modules, or Digital Light Processing (DLP) projectors can all be used, and the choice should consider how well the light interacts with the scene as well as any safety standards for eye exposure or industrial use.
The processing power behind a 3D imaging system is just as important as the cameras or sensors themselves. Depending on the imaging method you use, different types of computational hardware may be required. For real-time applications such as robotic vision, fast inspection, or time-sensitive feedback, edge FPGAs are ideal. They offer low-latency, parallel processing directly at the data source, reducing the need to send large volumes of data to a central processor. For more data-intensive tasks like volumetric video, computed tomography (CT), or photogrammetry involving many high-resolution images, powerful GPU clusters are often necessary. These can handle complex 3D reconstructions, large datasets, and advanced algorithms that require significant processing bandwidth. Choosing the right computational backend ensures your system performs efficiently and scales with your application’s demands.
As demands grow for spatially aware systems for automation, healthcare, robotics and more, 3D and volumetric imaging technologies are playing a foundational role. Understanding the strengths, limitations, and hardware dependencies of each method is key to building robust and scalable systems.
