# Tag Info

5

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.

4

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 ...

3

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 ...

2

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 ...

2

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 is ...

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 ...

2

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))...

1

They can. As Francesco mentioned, these problems can be solved with less correspondences. What makes the difference is how we formulate the problem. If we like a fast linear solution, then 8-points are required. For formulations using less number of points, the constraints are non-linear and typically involve either determinants or systems of polynomial ...

1

Hum, no A homography can be exactly fit to 4 point such that no three of them are collinear (example implementation in OpenCv). An essential matrix can be fit to the image of 5 non-coplanar points (implementation). A fundamental matrix can be fit to 7 points (implementation)

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 ...

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!

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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 ...

1

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 ...

1

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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Bundle adjustment is generally required for a multiview setting, where the number of cameras is large. In such regime, it is hard to exactly calibrate all the cameras extrinsically. Therefore, we instead look for an automatic procedure that could simultaneously refine the parameters of all the cameras towards the optimal. In the beginning stage, because of ...

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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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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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