structure-from-motion | 易学教程

How do I estimate positions of two cameras in OpenCV?

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问题 I have two sets of corresponding points from two images. I have estimated the Essential matrix which encodes the transformation between the cameras: E, mask = cv2.findEssentialMat(points1, points2, 1.0) I've then extracted the rotation and translation components: points, R, t, mask = cv2.recoverPose(E, points1, points2) But how do I actually get the cameras matrices of the two cameras so I can use cv2.triangulatePoints to generate a little point cloud? 回答1: Here is what I did: Input: pts_l -

Detecting/correcting Photo Warping via Point Correspondences

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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)

How to use own features, computed in openCV, in visualSFM pipeline

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问题 I try to do a 3D reconstruction from multiple images. I am currently using the visual SFM Pipeline that takes in Lowe SIFT features as .feat and .mat format. Both are binary so I could not read them with editor. Due to the documentation of visual sfm: Use your own feature matches 1. Write a txt file that contains all the feature matches 2. Load your images (with features) into VisualSFM 3. Use "SfM->Pairwise Matching->Import Feature Matches" 4. You may add feature matches by using the same

How do orthographic and perspective camera models in structure from motion differ from each other?

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问题 Under the assumption that the camera model is orthographic, how do orthographic and perspective camera models in structure from motion? Also, how do these techniques differ from each other? 回答1: Say you have a static scene and moving camera (or equivalently, rigidly moving scene and static camera) and you want to reconstruct the scene geometry and camera motion from two or more images. The reconstruction usually based on obtaining point correspondences, that is you have some equations which

Is Gaussian & Mean Curvatures Applicable for Rough Surfaces?

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问题 For a project I am working on, I have successfully performed the SFM procedure on road image data, and have been able to generate a .ply file containing point cloud coordinates (X, Y, Z), RGB values, and normals (nx, ny, nz). Now, I am interested in calculating curvature values for each point from the data I have. I have come across Surface Curvature MATLAB Equivalent in Python, but the implementation is said to work only when X, Y, and Z are 2D arrays. Is Gaussian and Mean curvatures

3D Reconstruction and SfM Camera Intrinsic Parameters

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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

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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

Is the recoverPose() function in OpenCV is left-handed?

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问题 I run simple test for OpenCV camera pose estimation. Having a photo and the same photo scaled up (zoomed in) I use them to detect features, calculate essential matrix and recover camera poses. Mat inliers; Mat E = findEssentialMat(queryPoints, trainPoints, cameraMatrix1, cameraMatrix2, FM_RANSAC, 0.9, MAX_PIXEL_OFFSET, inliers); size_t inliersCount = recoverPose(E, queryGoodPoints, trainGoodPoints, cameraMatrix1, cameraMatrix2, R, T, inliers); So when I specify the original image as the first

Is the recoverPose() function in OpenCV is left-handed?

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I run simple test for OpenCV camera pose estimation. Having a photo and the same photo scaled up (zoomed in) I use them to detect features, calculate essential matrix and recover camera poses. Mat inliers; Mat E = findEssentialMat(queryPoints, trainPoints, cameraMatrix1, cameraMatrix2, FM_RANSAC, 0.9, MAX_PIXEL_OFFSET, inliers); size_t inliersCount = recoverPose(E, queryGoodPoints, trainGoodPoints, cameraMatrix1, cameraMatrix2, R, T, inliers); So when I specify the original image as the first one, and the zoomed image as the second one, I get translation T close to [0; 0; -1]. However the

How do I estimate positions of two cameras in OpenCV?

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I have two sets of corresponding points from two images. I have estimated the Essential matrix which encodes the transformation between the cameras: E, mask = cv2.findEssentialMat(points1, points2, 1.0) I've then extracted the rotation and translation components: points, R, t, mask = cv2.recoverPose(E, points1, points2) But how do I actually get the cameras matrices of the two cameras so I can use cv2.triangulatePoints to generate a little point cloud? Yonatan Simson Here is what I did: Input: pts_l - set of n 2d points in left image. nx2 numpy float array pts_r - set of n 2d points in right