Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Depth accuracy of the stereo camera Ask Question. Asked 3 years, 3 months ago. Active 1 year, 8 months ago. Viewed 3k times. I have just callibrated 11 set of stereo images in matlab stereo calibration app and have got intrinsic and extrinsic parameters.
Is it possible to get the disparity error from this parameter?. Active Oldest Votes. Kozuch Kozuch 1 1 gold badge 7 7 silver badges 17 17 bronze badges. Luis Ortiz Luis Ortiz 1.
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I am doing a research in stereo vision and I am interested in accuracy of depth estimation in this question.
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It depends of several factors like:. My question is, what is the accuracy of depth estimation we can achieve in this field? Anyone knows a real stereo vision system that works with some accuracy? Can we achieve 1 mm depth estimation accuracy? I would add that using color is a bad idea even with expensive cameras - just use the gradient of gray intensity. Some producers of high-end stereo cameras for example Point Grey used to rely on color and then switched to grey.
Also consider a bias and a variance as two components of a stereo matching error. This is important since using a correlation stereo, for example, with a large correlation window would average depth i.
So there is always a trade-off. More than the factors you mentioned above, the accuracy of your stereo will depend on the specifics of the algorithm. It is up to an algorithm to validate depth important step after stereo estimation and gracefully patch the holes in textureless areas.
For example, consider back-and-forth validation matching R to L should produce the same candidates as matching L to Rblob noise removal non Gaussian noise typical for stereo matching removed with connected component algorithmtexture validation invalidate depth in areas with weak textureuniqueness validation having a uni-modal matching score without second and third strong candidates. This is typically a short cut to back-and-forth validationetc. The accuracy will also depend on sensor noise and sensor's dynamic range.
Thus there is a strong dependence of accuracy on the baseline and distance. Kinect will provide 1mm accuracy bias with quite large variance up to 1m or so.At first glance, the camera looked to be ruggedly packaged and a good match for autonomous vehicles and drones.
The compute efficiency noted that it takes about 7 ms to upload the images, 3 ms to rectify them, and then 22 ms to calculate the depth map on the Jetson TK1 GPU. In the background, the CPU runs a calibration check program. Given that real time sensing and obstacle detection must occur in 50 ms, this leaves about 18 ms to be used for obstacle detection. This looks like a nice fit for the Jetson TK1. I spoked a lot with the guys of StereoLab.
That camera seems really amazing. Only one concern about the distance of the two cameras. The cameras are 12 cm far, so the minimum measurable distance is over 1 meter. Really good for outdoor, but not useful for indoor operation used standalone. What is really amazing is the USB3.
I was just able to handle it for a few minutes, it seemed to be well packaged. Furthermore the algorithms used by StereoLabs developers generates a depth map so defined that I never saw in any other stereo vision system that I used in my past researches. Finally you stimulated my curiosity, so maybe that in the next days I will make a full test to understand the real definition capabilities of the camera.
Stay tuned Walter. However, note that this is not a currently shipping product. Yes, you can cross compile on an Ubuntu host and download the resulting program to the Jetson. Most people just develop on the Jetson as it is a capable development environment on its own, but there are a group of users who develop on a PC.
The cameras are nice, but the technical support is severely lacking. There is no phone number, and waiting for an email reply to a question can stretch to many days or weeks.Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv. Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly.
DOI: We present a method to evaluate stereo camera depth accuracy in human centered applications. It enables the comparison between stereo camera depth resolution and human depth resolution. Our method uses a multilevel test target which can be easily assembled and used in various studies. View on SPIE. Alternate Sources. Save to Library. Create Alert. Launch Research Feed. Share This Paper. Topics from this paper.
Stereo cameras Randomness Binocular vision Stereoscopic acuity Multimodal interaction. Citations Publications citing this paper. Hazard Relative Navigation : Towards safe autonomous planetary landings in unknown hazardous terrain Svenja Woicke Engineering Kochenderfer Computer Science ArXiv OrtizElizabeth V.
References Publications referenced by this paper. Camera Calibration Toolbox for Matlab. Bouget Feb Monocular zones in stereoscopic scenes: A useful source of information for human binocular vision? Julie M. Harris Physics, Engineering Electronic Imaging Edward SwanMark A.
Accuracy, Precision and Repeatability of 3D Depth Cameras
Already on GitHub? Sign in to your account. I've noticed some depth issues with my ZEDs, which had abysmal depth errors with the factory calibration, i. To test the depth, we used a planar and highly textured surface and moved it away from the camera 50 times 1m to 25msaving the measurements via SVOs p mode.
On the ZED, we mounted a small laser rangefinder which looked in the same direction. Then, I ran through every SVO file and chose the same pixel, whose ray hit the target every frame close to the red laser dot. The measurement is the mean of all disparities during the SVO sequencewhich would help the accuracy greatly. Then I used a manual calibration file to improve the results, which are plotted in the image below.
I also ran ELAS on the rectified images using the rectified intrinsics to test whether your algorithm is just wrong. It does not seem that way:. Is there any way to improve the depth accuracy? But I would assume that the ZED would fluctuate around the laser ground truth and not be systematically off, i.
Your charts show a quadratic decrease in Z-accuracy and a bias increase with the range, which is consistent with the stereo uncertainty model. The systematic bias you mention is due to subpixel interpolation which is affected by pixel-locking and foreshortening phenomena. These phenomena are discussed in many papers. We are working on bias reduction techniques but it is still a work in progress. Regarding calibrationyour camera may have suffered a mechanical shock or there was an issue during its calibration process.
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For the recalibration, your charts seem to indicate that you are close to the stereo accuracy model. You can still try Kalibr which is currently the state of the art tool for calibration and could improve your triangulation results.
I got a simillar behaviour. What I did to solve this is to calibrate the camera using external ROS calibrator It works much better than the calibration tool. Do you have a link to the external ROS calibrator? LuisOrtizF The link doesn't work for me, could you share the title and author name please?
I am curious what the best depth accuracy you can achieve using ZED camera at 1m - 2m distance range. This depends on the calibration fx and B and the quality of the stereo matching. So 1m with good stereo matching and perfect calibration, most measurements will be below 1 cm. But keep in mind that the stereo matching may be biased in some way. Did you eliminate the estimation error with manual calibration?On dictionary, the two terms are defined mutually, so they are easily mistaken to be the same thing.
Actually, in the fields of science, engineering, and statistics, they refer to different things. Both accuracy and precision are used in the field of measurement. Accuracy is the closeness of a measured value to a true value, while precision is the closeness of a measured value to other measured values.
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A good example is the measurement of a shot on target. As shown in the pictures below, a represents both high accuracy and high precision, because the shots are close to the center and each other. Therefore, a measurement can be both accurate and precise, or neither, or accurate but not precise, or precise but not accurate.
It should be noted that repeatability, a concept relevant to precision, is also frequently used, particularly in descriptions of industrial-grade mechanical products. It refers to the closeness among successive measurements carried out with the same method by the same operator under identical conditions. In 3D depth sensing, accuracy and precision are often measured at submillimeter level, used to represent the quality and stability of 3D outputs. For industrial-grade 3D depth camera, which strictly requires higher level of accuracy, precision or repeatability becomes a highlighted parameter, since it signifies the stability and reliability of the camera.
Share This Article! About the Author: Alison Bennett. Related Posts. Point Cloud and 3D Image. How does Light Influence 3D Scanning? Only Registered user can Download.We achieve this through a variety of sensors and cameras feeding data into custom software, running on bespoke computing hardware, and outputting to any number of display or projection devices.
Because it all begins with the sensing technologies, we spend plenty of time evaluating various products that help us determine how people move through a space.
Follow stimulant or our RSS feed to be notified. Tara is a stereo camera from e-con Systems. The two sensors are synced on the device and are delivered together as a single side-by-side image. The camera is backwards-compatible with USB 2.Anytime Stereo Image Depth Estimation on Mobile Devices
More advanced image analysis such as skeleton tracking or facial feature tracking would need to be provided by a secondary toolkit. The physical design of the Tara is intended for very light use as its cast acrylic case has plastic mounting threads, and its lenses are exposed with no shielding.
Tara is a good choice for medium-range indoor applications that can take advantage of ambient-lit stereo pair images where detailed image analysis is not required or is provided by another toolkit. The Structure sensor is designed to attach physically to iOS devices to provide 3D scanning capabilities and enable mixed reality scenarios. This allows for complete system with low power consumption and a small form factor.
Many of the examples have not been ported over to Android or ARM Linux and documentation is very sparse so be prepared to go digging in the forums if you have an issue. One of the most exciting features was that we were able to stream the a depth image and point cloud over the network using ROS and the gigabit ethernet link. The ability to simply stream depth data over the network resolves a key pain point for many of our projects, namely USB extension.
The SR is the spiritual successor to the F The SR does everything the F does but with better quality and accuracy. We found the depth feed from this camera less noisy than that from the F So even though though they have the same resolution the SR performed significantly better at tasks such as 3D face tracking.
A nice feature was the removable USB3 cord which allows users to use a longer or shorter cords based on their needs.
The Intel RealSense SR is good for medium-range indoor applications developed in a variety of frameworks, especially for tracking faces or for augmented reality experiences. Orbbec is the newest entrant into the 3D camera space, but the team has been at it for a while. Support for openFrameworks, Cinder, and Unity 3D is said to be forthcoming. The SDK supports basic hand tracking which can be used for gestural interfaces, but not full skeleton tracking. The unit can sense as far as 8 meters away, which beats the range of most other sensors.
The SDK supports face and expression tracking, but not hand tracking or full skeletons. The device really comes into its own when the camera in motion for augmented reality or 3D scanning applications. The Intel RealSense R is good for medium-range indoor applications developed in a variety of frameworks, especially for tracking faces or for augmented reality experiences. The Stereolabs ZED product is unique among this list as it does not use infrared light for sensing, but rather a pair of visible light sensors to produce a stereo image, which is then delivered to software as a video stream of depth data.
While the hardware is quite powerful, the provided SDK is pretty limited to simply capturing the depth stream, without any higher-level interpretation. Any tracking of objects, hands, faces, or bodies would need to be implemented by the developer. The Zed Stereo camera is great for high frame rate, outdoor, or long range applications which only require a raw depth stream.
The F version of the RealSense product is meant to be front-facing, and excels at tracking faces, hands, objects, gestures, and speech. The Intel RealSense F is a good choice for short-range applications that rely on tracking the face and hands of a single user. For all that, you get a wider field of view and very clean depth data at a range of. The Kinect for XBox One is great for medium-range tracking of multiple skeletons and faces in a space, and works with most popular application frameworks, but the sensor must be located close to the host computer.
The DUO mini lx camera is a tiny USB-powered stereo infrared camera that provides high-frame-rate depth sensing to a range of about 3m. It includes IR emitters for indoor use, but can be run in a passive mode to accept ambient infrared light — meaning it can be used outdoors in sunlight.