3d-reconstruction

Detecting/correcting Photo Warping via Point Correspondences

由 萝らか妹 提交于 2019-12-13 06:34:40
问题 I realize there are many cans of worms related to what I'm asking, but I have to start somewhere. Basically, what I'm asking is: Given two photos of a scene, taken with unknown cameras, to what extent can I determine the (relative) warping between the photos? Below are two images of the 1904 World's Fair. They were taken at different levels on the wireless telegraph tower, so the cameras are more or less vertically in line. My goal is to create a model of the area (in Blender, if it matters)

Retrieving the original coordinates of a pixel taken from a warped Image

由 橙三吉。 提交于 2019-12-12 15:04:37
问题 I have four corners extracted from a sourceImage : src_vertices[0] = corners[upperLeft]; src_vertices[1] = corners[upperRight]; src_vertices[2] = corners[downLeft]; src_vertices[3] = corners[downRight]; These four corners are warped to destinationImage like that: dst_vertices[0] = Point(0,0); dst_vertices[1] = Point(width, 0); dst_vertices[2] = Point(0, height); dst_vertices[3] = Point(width, height); Mat warpPerspectiveMatrix = getPerspectiveTransform(src_vertices, dst_vertices); cv::Size

Specifications of Checkerboard (Calibration) for obtaining maximum accuracy in stereo reconstruction

由 隐身守侯 提交于 2019-12-11 04:49:23
问题 I have to reconstruct an object which will be placed around 1 meter to 1.5 meters away from the baseline of my stereo setup. The image captured by both cameras have high resolution (10 MP) The accuracy with which I have to detect it's position is +/- 0.5mm, in all the three co-ordinate axes. (If you require more details, please let me know) For these, what should the optimal specifications of my checkerboard (for calibration) be? I only know that it should be an asymmetric board. It should be

transforming projection matrices computed from trifocal tensor to estimate 3D points

由 久未见 提交于 2019-12-09 13:05:24
问题 I am using this legacy code: http://fossies.org/dox/opencv-2.4.8/trifocal_8cpp_source.html for estimating 3D points from the given corresponding 2D points from 3 different views. The problem I faced is same as stated here: http://opencv-users.1802565.n2.nabble.com/trifocal-tensor-icvComputeProjectMatrices6Points-icvComputeProjectMatricesNPoints-td2423108.html I could compute Projection matrices successfully using icvComputeProjectMatrices6Points . I used 6 set of corresponding points from 3

3D Reconstruction and SfM Camera Intrinsic Parameters

由 不问归期 提交于 2019-12-08 04:00:27
问题 I am trying to understand the basic principles of 3D reconstruction, and have chosen to play around with OpenMVG. However , I have seen evidence that the following concepts I'm asking about apply to all/most SfM/MVS tools, not just OpenMVG. As such, I suspect any Computer Vision engineer should be able to answer these questions, even if they have no direct OpenMVG experience. I'm trying to fully understand intrinsic camera parameters , or as they seem to be called, " camera instrinsics ", or

3D Reconstruction and SfM Camera Intrinsic Parameters

由 混江龙づ霸主 提交于 2019-12-06 15:10:11
I am trying to understand the basic principles of 3D reconstruction, and have chosen to play around with OpenMVG . However , I have seen evidence that the following concepts I'm asking about apply to all/most SfM/MVS tools, not just OpenMVG. As such, I suspect any Computer Vision engineer should be able to answer these questions, even if they have no direct OpenMVG experience. I'm trying to fully understand intrinsic camera parameters , or as they seem to be called, " camera instrinsics ", or " intrinsic parameters ". According to OpenMVG's documentation, camera intrinsics depend on the type

3D reconstruction from 2 images with baseline and single camera calibration

由 人走茶凉 提交于 2019-12-06 12:16:15
问题 my semester project is to Calibrate Stereo Cameras with a big baseline (~2m). so my approach is to run without exact defined calibration pattern like the chessboard cause it had to be huge and would be hard to handle. my problem is similar to this: 3d reconstruction from 2 images without info about the camera Program till now: Corner detection left image goodFeaturesToTrack refined corners cornerSubPix Find corner locations in right image calcOpticalFlowPyrLK calculate fundamental matrix F

Compute fundamental matrix without point correspondences?

由 戏子无情 提交于 2019-12-06 02:26:25
问题 I would like to verify that my understanding of the fundamental matrix is correct and if it's possible to compute F without using any corresponding point pairs. The fundamental matrix is calculated as F = inv(transpose(Mr))*R*S*inv(Ml) where Mr and Ml are the right and left intrinsic camera matrices, R is the rotation matrix that brings the right coordinate system to the left one, and S is the skew symmetric matrix S = 0 -T[3] T[2] where T is the translation vector of the right coordinate

OpenNI Intrinsic and Extrinsic calibration

由 穿精又带淫゛_ 提交于 2019-12-05 14:13:05
How would one extract the components of the intrinsic and extrinsic calibration parameters from OpenNI for a device such as the PrimeSense? After some searching I only seem to find how to do it through ROS, but it is not clear how it would be done with just OpenNI. 来源： https://stackoverflow.com/questions/41110791/openni-intrinsic-and-extrinsic-calibration

3D reconstruction from 2 images with baseline and single camera calibration

由 左心房为你撑大大i 提交于 2019-12-04 21:26:07
my semester project is to Calibrate Stereo Cameras with a big baseline (~2m). so my approach is to run without exact defined calibration pattern like the chessboard cause it had to be huge and would be hard to handle. my problem is similar to this: 3d reconstruction from 2 images without info about the camera Program till now: Corner detection left image goodFeaturesToTrack refined corners cornerSubPix Find corner locations in right image calcOpticalFlowPyrLK calculate fundamental matrix F findFundamentalMat calculate H1, H2 rectification homography matrix stereoRectifyUncalibrated Rectify
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