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Stereo imaging Given the large field of view you need in relation to the accuracy you want, and how close you want to be, I think that stereo imaging may be a challenging, so you need to somehow amplify the differences you are trying to measure. Structured lighting If you are essentially trying to measure the profile of an object, have you considered a ...


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From a more pragmatic than philosophical perspective, possibilities include: Comparing with ground truth data--these data could come from surveying's instruments, or be assumed to be "reliable" (e.g. you could assess the system's accuracy on the Eiffel tower and other well-known buildings for which measurements have been curated); Verifying physical ...


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For very fine resolution your best bet is likely a cheap and readily available laser depth gauge from Keyence. They work, they're relatively cheap, and they're an industry standard. http://www.keyence.com/products/measure/laser/laser.php The cheapest 2D optical technique could be to create a "shadow Moire" system using Ronchi rulings. With the guidance of ...


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"Looking-in" simply means that the cameras will need to be angled such that their principal axes cross over at some point in front of the camera (rather than being parallel as is often assumed for stereo work) The reason for doing this when the cameras are far apart is also simple: Stereo requires you to match features from one image to the other. Hence ...


3

First of all, our brain does not only rely on our stereo visionary system to estimate the depth. There are many cues in a image scence for depth estimation, of which stereo, vision belongs to a sub-type called Binocular cues. Technically there are many other methods of depth estimation, like Structure from motion, Perspective,etc. Just take a look at here ...


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I've worked in metrology in a past life. Systems like this one both use the stereoscopy and claim to achieve about 1 microns precision (sub-pixel accuracy). The solution with a laser scanner and an encoder would be another solution. My job was to test those systems. It was not possible to achieve desired precision reliably. In fact, most vendors were ...


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I guess this is a straightforward non-linear optimization problem (to be solved with Newton variations, such as Trust-Region methods), where you don't even need to compute the Jacobian analytically. It appears to me that the optimization problem is written over $K_i$, and thus is the input to the cost function. To compute the cost, at each call to this ...


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If you know the radial distortion parameters (e. g. by calibrating the cameras), then you should simply compensate for the distortion before you do structure from motion. You can either undistort the images before you do anything else, or you can undistort the coordinates of the points that you are trying to match.


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If you move your head (or if the target objects move against each other and/or the far background), you get stereoscopic data over time. Perhaps using some sort of visual memory to compare scene data between/against/across... So if the robot can move (or can move its camera(s))...


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It is because in the case of fundamental matrix, each correspondence point relates to only one constraint(i.e it maps a point to a line in other image). Hence 8 correspondence points are required. But in the case of homography, each correspondence solves two constraints. Hence only 4 correspondence points are sufficient.


2

Before going in to the details about 'why we need two cameras' part, the reason you can observe a 3D environment even with one eye is because even while using one eye, without any conscious effort due to head and eye movement the field of view changes very slightly and rapidly. This rapid movement of eye enables brain to receive images with little ...


2

Basically, two cameras are not "really needed" for the traditional perception of 3D vision. Access to two types of slightly different images is a common method. They can have different points of view produced by the same camera, and can be interleaved in time. But a one-eyed person can also get a sense of depth through other senses or modalities plus models ...


1

... if the camera's intrinsics are known and calibrated, is it possible to generate the depthmap fully automatically through a graycode sequence without any manual calibration process ? Yes, provided that you are talking about a stereo-vision system. Simply extracting the depth information, does not require knowledge of the camera model. It only requires ...


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There is a related situation in bearings only tracking known as the observably condition. For a single sensor, observing a single object, in general terms, the observer must be able to maneuver more than the object. A fully rigorous treatment involves Lie algebras, of which a Google Scholar search will produce many references, most more advanced than my ...


1

When you say cameras are parallel it only means that the center pixels are parallel. Every pixel has a different angle, and that refers to the overlapping regions you were talking about. Actually there is at most one overlapping voxel (3d pixel kind of) for every 2 pixels! There is more about it in epipolar geomtry , it's interesting and pretty simple!


1

You might consider a particle filter. Here's a link to a paper I wrote about tracking objects in video using a particle filter. The great thing about these is that objects can be tracked through temporary occlusions. The trick with using a Kalman filter here is dealing with the nonlinearity introduced by the edges of the video field, and casting your ...


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if it is possible to employ standard stereo matching algorithms like block-matching to images taken in the NIR spectrum and if so would it be possible (of course depending on the conditions) to obtain a dense depth map. Yes, it is possible. The Leap sensor operates at 850 nm and it is composed of two wide angle cameras that track hands (specifically) in 3D ...


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I have a collection of 170 reflectance spectra of various "materials" (such as ripe brown banana and asphalt) attributed to Ron Gershon of Eastman Kodak. I believe they are diffuse reflectance, which would be zero for a perfect mirror. Here is a plot of all the spectra in the dataset: Figure 1. All reflectance spectra Here is a plot of the mean and ...


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As far as I know, it is a common practice to use regular image processing techniques in this field. In fact I have recently finished a project in this field. if it is possible to employ standard stereo matching algorithms like block-matching to images taken in the NIR spectrum and if so would it be possible (of course depending on the conditions) to ...


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Calibrate your cameras with the same world coordinate system. Use the same checkerboard and take images at the same time with both cameras. After you have your extrinsic matrices for each camera, you can use this answer here where you need to solve the equation for the position of each camera center in world coordinate system. Then you can calculate the ...


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There two ways to look at this problem. In simple terms, image rectification warps both images onto a common coordinate frame by typically estimating the transformation using the epipolar geometry. Image alignment finds the transformation from one image to the other. It doesn't guarantee any constrains on the epipolar geometry and only one single image ...


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If all you want is to reconstruct a scene from a pair of images from a pair of calibrated stereo cameras, and your calibration is sufficiently accurate, then you do not need bundle adjustment. You do need bundle adjustment if you want to reconstruct a scene from a sequence of images or for a sequence of stereo pairs, where the camera poses in each view are ...


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It was the way I was setting principal points during rectification, to center the images back for the warping. If I have same principal points in both cameras I only get positive disparities, as expected. If cameras are parallel, it is not possible to have both negative and positive disparities. That only happens with converging stereo cameras (negative ...


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Yes, it is possible. It often happens when you do uncalibrated stereo rectification. To calculate the depth, you would have to add the minimum disparity value to you disparity map, to shift the range so that it starts with 0.


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First, on distinguishing low contrast features from noise, then on a possible alternate course of action to solve what I think you are trying to do. From my experience, distinguishing between noise and low contrast features is a hard thing to do. The problem arises quite often when you try to do edge detection on an image. Using the most common algorithms ...


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To start with, you might try a reconstruction of different object coordinates from each pair (or small subset) of photos. Then, given a large enough set of reconstructions using different pairs or subsets, look at the statistical distribution and variance of all the different reconstructed coordinates.


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