{"id":3629,"date":"2026-10-29T14:35:08","date_gmt":"2026-10-29T06:35:08","guid":{"rendered":"http:\/\/www.familiasviajeras.com\/blog\/?p=3629"},"modified":"2026-10-29T14:35:08","modified_gmt":"2026-10-29T06:35:08","slug":"how-does-the-occlusion-of-the-object-affect-the-3d-scanning-result-of-a-structured-light-4462-cac09c","status":"publish","type":"post","link":"http:\/\/www.familiasviajeras.com\/blog\/2026\/10\/29\/how-does-the-occlusion-of-the-object-affect-the-3d-scanning-result-of-a-structured-light-4462-cac09c\/","title":{"rendered":"How does the occlusion of the object affect the 3D scanning result of a Structured Light 3D Camera?"},"content":{"rendered":"

As a senior engineer and business lead at a structured light 3D camera supplier, I\u2019ve spent the last seven years troubleshooting field deployments\u2014helping 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: \u201cOur scan of the engine block came out with a hole right where the fuel injector should be\u201d or \u201cWe scanned a patient\u2019s prosthetic socket, and it looks like the contact points are missing entirely.\u201d Nine times out of ten, the culprit isn\u2019t a faulty camera or bad calibration\u2014it\u2019s occlusion. Structured Light 3D Camera<\/a><\/p>\n

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Occlusion is often mischaracterized as a minor flaw, a \u201cglitch\u201d that can be fixed with a better software filter or a re-scan. But from my team\u2019s 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\u2019t apply to other 3D scanning technologies like LiDAR or stereo vision.<\/p>\n

Structured light cameras work by projecting a known pattern of light\u2014usually a grid of horizontal and vertical lines, or in our flagship units, a series of Gray-coded binary patterns and phase-shifted stripes\u2014onto 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\u2019s depth using triangulation. The offset between projector and camera is non-negotiable\u2014it\u2019s what makes structured light so precise (our units hit 0.05mm accuracy at 1m), but it\u2019s also what creates occlusion.<\/p>\n

Let\u2019s 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\u2019re 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\u2019s 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\u2019s gloved hand leaning in to adjust a part mid-scan.<\/p>\n

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\u2014so 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\u2019s 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\u2019s far wall is blocked by the hole\u2019s lip. The result is a flat, 2D black hole in the scan, not a concave cavity. That\u2019s not a camera error\u2014it\u2019s the geometry of the projection.<\/p>\n

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\u2014say, a 2mm edge on a plastic bracket\u2014might only create a tiny \u201cnotch\u201d in the scan, but deep, narrow occlusions, like the gap between a car\u2019s door and its frame, can create gaps of 5mm or more that make the scan useless for CAD design.<\/p>\n

Another common misconception is that multiple scans from different angles will fix all occlusion. While that\u2019s partially true, it\u2019s 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 \u201cdead zones\u201d) can\u2019t be filled. Even with 12 scans, some tight internal features\u2014like the threaded bore of a bolt that\u2019s aligned directly between two scan points\u2014will stay hidden. The client\u2019s fix wasn\u2019t 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.<\/p>\n

Occlusion also interacts with material properties in ways that amplify its impact. Dark, absorbent materials like carbon fiber or cured rubber don\u2019t just distort the light pattern\u2014they 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\u2019s skin and its internal ribbing were completely unreadable because the carbon absorbed the projector\u2019s infrared light (our standard pattern uses near-IR for better penetration). The fix there was adjusting the pattern\u2019s wavelength to visible light, which bounced off the carbon surface enough for the camera to pick up partial data from the gaps\u2014but it still required a supplementary scan from a lower angle to capture the full depth.<\/p>\n

As a supplier, we don\u2019t just sell cameras\u2014we solve these problems. Over the last three years, we\u2019ve integrated two key features into our latest line of structured light cameras to mitigate occlusion, and they\u2019re 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\u2019s 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\u2019s software adjusts the duration of each pattern based on the surface\u2019s 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.<\/p>\n

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\u2019s residual limb for a custom prosthetic socket. The narrow, curved interior is a textbook case of extreme self-occlusion\u2014no camera, no matter how many projectors it has, can get a line of sight to every part of the socket\u2019s 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.<\/p>\n

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\u2019t understand how occlusion interacts with the system\u2019s setup.<\/p>\n

As structured light technology becomes more common in consumer products, from phone face unlock to furniture assembly scanners, we\u2019re 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\u2019t capture the dovetail joints on their custom cabinets. The problem? The dovetail\u2019s internal angles created self-occlusion, and the single projector in their consumer scanner couldn\u2019t reach the recessed edges. Our solution was to rent them a mid-range industrial structured light unit with two projectors for a weekend\u2014for a fraction of the cost of a new scanner\u2014and they were able to get precise scans to machine replacement parts.<\/p>\n

So what does all this mean for anyone relying on structured light 3D scanning? Occlusion isn\u2019t 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\u2019s 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.<\/p>\n

At our company, we\u2019ve built our reputation on not just selling hardware, but on walking clients through every step of their scanning process\u2014from setup to post-processing\u2014because we know that a scan is only as good as its ability to account for occlusion. If you\u2019re working on a project where gaps in 3D scan data could lead to costly mistakes, whether that\u2019s 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.<\/p>\n

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Feel free to reach out to our team to schedule a consultation and test our structured light camera line for your application.<\/p>\n

Ceramic Circuit Board Inspection<\/a> References:
\nGorthi, S. S., & Rastogi, P. (2010). Fringe projection techniques: going beyond 2D shape measurement. Optics and Lasers in Engineering<\/em>.
\nSalvi, J., Fernandez, S., Pribanic, T., & Llado, X. (2010). A state of the art in structured light patterns for surface profilometry. Pattern Recognition<\/em>.
\nZhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence<\/em>.<\/p>\n

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Zhejiang Hanchine Al Technology Co., Ltd.<\/a>
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.
Address: 3-806, Lvchuang Plaza, Yuhang District, Hangzhou
E-mail: alisa.zhang@hanchine.com
WebSite: https:\/\/www.hanchine.com\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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