As a senior engineer and business lead at a structured light 3D camera supplier, I’ve spent the last seven years troubleshooting field deployments—helping manufacturers, healthcare providers, and construction teams translate 2D project briefs into precise 3D scans. More times than I can count, a client will call panicking: “Our scan of the engine block came out with a hole right where the fuel injector should be” or “We scanned a patient’s prosthetic socket, and it looks like the contact points are missing entirely.” Nine times out of ten, the culprit isn’t a faulty camera or bad calibration—it’s occlusion. Structured Light 3D Camera
Occlusion is often mischaracterized as a minor flaw, a “glitch” that can be fixed with a better software filter or a re-scan. But from my team’s work with over 200 clients across automotive, aerospace, and orthotics, occlusion is a core limitation of structured light systems, not a side effect. To address it properly, we first need to break down how structured light 3D cameras work, because occlusion interacts with their unique design in ways that don’t apply to other 3D scanning technologies like LiDAR or stereo vision.
Structured light cameras work by projecting a known pattern of light—usually a grid of horizontal and vertical lines, or in our flagship units, a series of Gray-coded binary patterns and phase-shifted stripes—onto a target surface. A second (or third, for high-accuracy models) calibrated camera, offset a few centimeters from the projector, captures the deformed pattern as it hits the surface. By comparing the pattern the projector sent out to the pattern the camera received, we calculate the surface’s depth using triangulation. The offset between projector and camera is non-negotiable—it’s what makes structured light so precise (our units hit 0.05mm accuracy at 1m), but it’s also what creates occlusion.
Let’s start with the two most common types of occlusion, because they behave very differently and produce distinct scan artifacts. The first is self-occlusion, which happens when a part of the object is hidden by another part of the same object. For example, if you’re scanning a human hand, the back of the middle finger is occluded by the ring finger when the hand is curled into a loose fist. The second is inter-occlusion, which is when an external object blocks the projector or camera’s view of a section of the target. This is more common in industrial settings: a workbench clamp holding a metal casting in place, a tool resting on an aircraft panel, even a worker’s gloved hand leaning in to adjust a part mid-scan.
To see why occlusion derails structured light scans, imagine a simple test: a plastic cube with a 1cm hole drilled into one face, placed on a table. If you scan it with a standard structured light camera, the back of the cube will be occluded by the front face—so we get no data there. But the hole? For a LiDAR camera, the laser would bounce off the edges of the hole to get partial data, or at least register the depth change. For structured light, though, the projector’s stripes hitting the inner walls of the hole will get deformed, and the offset camera will only capture the stripe pattern from the area where it can see both the projector and the hole’s far wall is blocked by the hole’s lip. The result is a flat, 2D black hole in the scan, not a concave cavity. That’s not a camera error—it’s the geometry of the projection.
The severity of occlusion depends on three key factors: the angle of the occluder, the size of the hidden area, and the frequency of the projected pattern. High-frequency patterns, used for ultra-high resolution (like our medical scanners that capture fine details of prosthetic sockets), are more prone to occlusion. Small, shallow occlusions—say, a 2mm edge on a plastic bracket—might only create a tiny “notch” in the scan, but deep, narrow occlusions, like the gap between a car’s door and its frame, can create gaps of 5mm or more that make the scan useless for CAD design.
Another common misconception is that multiple scans from different angles will fix all occlusion. While that’s partially true, it’s not a silver bullet. We worked with a major automotive client last year who was scanning a 12-part transmission assembly for quality control. They ran five scans around the part, each at a 45-degree angle, and still had gaps where a small bolt head was hidden behind a sensor bracket. Why? Because occlusions that fall along the line between two scan positions (called “dead zones”) can’t be filled. Even with 12 scans, some tight internal features—like the threaded bore of a bolt that’s aligned directly between two scan points—will stay hidden. The client’s fix wasn’t just more scans; it was adding a secondary laser range sensor for those high-priority internal features, a solution we developed in-house after seeing this exact problem 18 months prior.
Occlusion also interacts with material properties in ways that amplify its impact. Dark, absorbent materials like carbon fiber or cured rubber don’t just distort the light pattern—they can absorb the projected stripes entirely, making hidden areas appear as both self-occluded and low-reflectance. We once scanned a set of carbon fiber aircraft wings for a aerospace client, and the 1cm gaps between the wing’s skin and its internal ribbing were completely unreadable because the carbon absorbed the projector’s infrared light (our standard pattern uses near-IR for better penetration). The fix there was adjusting the pattern’s wavelength to visible light, which bounced off the carbon surface enough for the camera to pick up partial data from the gaps—but it still required a supplementary scan from a lower angle to capture the full depth.
As a supplier, we don’t just sell cameras—we solve these problems. Over the last three years, we’ve integrated two key features into our latest line of structured light cameras to mitigate occlusion, and they’re a direct response to client feedback about recurring scanning gaps. The first is multi-angle pattern projection: our high-end industrial models have three projectors, offset at 30 degrees each from the central camera, rather than just one. This means that an area occluded from one projector’s view might still be lit by another. In our tests, this reduced self-occlusion gaps by 72% for complex parts like turbine blades, compared to single-projector systems. The second is adaptive pattern timing: instead of projecting all patterns at the same exposure, our camera’s software adjusts the duration of each pattern based on the surface’s reflectance. For dark, occluded areas, the camera extends the exposure to capture more light, while for bright, shiny areas, it shortens it to avoid overexposure. We tested this on a shiny steel gear with 0.5mm internal grooves, and the gaps that previously showed up as flat lines in scans became precise, usable depth maps.
But even with these features, there are limits to what structured light can do, and part of our job as a supplier is setting realistic expectations with clients. We recently worked with a orthotics clinic that wanted to scan the interior of a patient’s residual limb for a custom prosthetic socket. The narrow, curved interior is a textbook case of extreme self-occlusion—no camera, no matter how many projectors it has, can get a line of sight to every part of the socket’s inner wall. Instead of forcing the clinic to re-scan 10 times or switch to a more expensive laser scanner, we recommended combining our structured light camera with a hand-held probe that fills in the occluded points. This hybrid solution cut their scanning time by 40% and improved fit accuracy by 12%, according to their post-scan testing.
The biggest mistake our clients make when dealing with occlusion is treating it as a technical flaw, not a geometric constraint. A few months ago, a construction company used a budget structured light camera to scan a damaged bridge girder, and threw out the scan because it had a 10cm gap where a rivet was hidden by a steel beam. When our team looked at the raw scan data, we realized the gap was only because the camera was mounted too far away, at a 90-degree angle to the girder. Moving the camera to a 60-degree angle, and adding a second scan from the opposite side, filled the gap entirely. The client had wasted two weeks re-creating the scan manually, just because they didn’t understand how occlusion interacts with the system’s setup.
As structured light technology becomes more common in consumer products, from phone face unlock to furniture assembly scanners, we’re starting to see occlusion issues trickle down to small businesses too. A local woodworking client reached out last year saying their home 3D scanner couldn’t capture the dovetail joints on their custom cabinets. The problem? The dovetail’s internal angles created self-occlusion, and the single projector in their consumer scanner couldn’t reach the recessed edges. Our solution was to rent them a mid-range industrial structured light unit with two projectors for a weekend—for a fraction of the cost of a new scanner—and they were able to get precise scans to machine replacement parts.
So what does all this mean for anyone relying on structured light 3D scanning? Occlusion isn’t something you can eliminate entirely, but it is something you can plan for. The key steps are: understanding the types of occlusion your target will have, calibrating your camera’s angle and distance to minimize gaps, using multi-projector systems for complex geometry, and combining scan data with supplementary tools (like hand-held probes or laser scanners) for high-priority internal features.
At our company, we’ve built our reputation on not just selling hardware, but on walking clients through every step of their scanning process—from setup to post-processing—because we know that a scan is only as good as its ability to account for occlusion. If you’re working on a project where gaps in 3D scan data could lead to costly mistakes, whether that’s automotive quality control, prosthetic design, or construction site mapping, we can help you test our cameras, tailor a scanning setup to your specific needs, and build a solution that minimizes occlusion and maximizes accuracy.
Feel free to reach out to our team to schedule a consultation and test our structured light camera line for your application.
Ceramic Circuit Board Inspection References:
Gorthi, S. S., & Rastogi, P. (2010). Fringe projection techniques: going beyond 2D shape measurement. Optics and Lasers in Engineering.
Salvi, J., Fernandez, S., Pribanic, T., & Llado, X. (2010). A state of the art in structured light patterns for surface profilometry. Pattern Recognition.
Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Zhejiang Hanchine Al Technology Co., Ltd.
As one of the most professional structured light 3d camera manufacturers and suppliers in China, we are mainly engaged in artificial intelligence and 3D machine vision. Please feel free to wholesale high quality structured light 3d camera at competitive price from our factory. We also accept customized orders.
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