stereo-3d

Get textured pointcloud with Block-Matching-Algorithm

由 老子叫甜甜 提交于 2019-12-08 09:25:32
问题 I want to texture a generated pointcloud with the original image color from two images. For this I calculated the disparity-map with Block-Matching and did the reconstruction. Also writing an export function for .ply-files wasn't a big deal. My problem is: How do I get the color from block-matching-algorithm? It does look for similar pixels on rectified images, but there is no variable which saves the position of a found matching, referred to API. Afterward it is not possible to recover the

Calculate Depth map of right image

由 北城以北 提交于 2019-12-08 04:32:14
问题 What I have done: To create a novel view(right image) from left image(given), I used the formula for pure translation between views(from Zissermann book) as: x' = x + K.t/Z x'= [u' v' 1]; x = [u v 1]; K = [f 0 cx; 0 f cy;0 0 1]; t = [t1 t2 t3]^T; Z - depth of pixel in left image The images and camera matrix were taken from Middlebury stereo 2014 dataset. By implementing this, I have an image with holes(black regions) due to disocclusions. What I need to do : To fill these holes, several

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

Python 2.7/OpenCV 3.3: Error in cv2.initUndistortRectifyMap . Not showing undistort rectified images

由 孤街醉人 提交于 2019-12-06 07:42:59
I want to distort and rectified my stereo images. For which I used Opencv 3.3 in Python 2.7. Code I used is : import cv2 import numpy as np cameraMatrixL = np.load('mtx_left.npy') distCoeffsL = np.load('dist_left.npy') cameraMatrixR = np.load('mtx_right.npy') distCoeffsR = np.load('dist_right.npy') R = np.load('R.npy') T = np.load('T.npy') imgleft = cv2.imread('D:\python\camera calibration and 3d const\left\left60.png',0) imgright = cv2.imread('D:\python\camera calibration and 3d const\Right/right60.png',0) R1,R2,P1,P2,Q,validPixROI1, validPixROI2 = cv2.stereoRectify(cameraMatrixL,distCoeffsL

OpenCV - Tilted camera and triangulation landmark for stereo vision

由 守給你的承諾、 提交于 2019-12-06 04:42:48
问题 I am using a stereo system and so I am trying to get world coordinates of some points by triangulation. My cameras present an angle, the Z axis direction (direction of the depth) is not normal to my surface. That is why when I observe flat surface, I get no constant depth but a "linear" variation, correct? And I want the depth from the baseline direction... How I can re-project? A piece of my code with my projective arrays and triangulate function : #C1 and C2 are the cameras matrix (left and

Stereoscopic 3D on WPF

由 [亡魂溺海] 提交于 2019-12-06 02:13:29
问题 I have to show stereoscopic 3D graphics on a WPF control. I already have the code which create two DirectX-9 textures to show, one texture for each eye. I want to use 3D Vision (not anaglyph). I considered the following ways to show the two pictures as 3D stereo: Using OpenGL or DirectX 11.1 Stereo API. Using NvAPI_Stereo_SetActiveEye as described here: http://www.nvidia.com/docs/IO/40505/WP-05482-001_v01-final.pdf Using NVidia stereo signature as described here: NV_STEREO_IMAGE_SIGNATURE and

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

Python - Perspective transform for OpenCV from a rotation angle

由 自古美人都是妖i 提交于 2019-12-04 21:02:59
问题 I'm working on depth map with OpenCV . I can obtain it but it is reconstructed from the left camera origin and there is a little tilt of this latter and as you can see on the figure, the depth is "shifted" (the depth should be close and no horizontal gradient): I would like to express it as with a zero angle, i try with the warp perspective function as you can see below but i obtain a null field... P = np.dot(cam,np.dot(Transl,np.dot(Rot,A1))) dst = cv2.warpPerspective(depth, P, (2048, 2048))

Is stereoscopy (3D stereo) making a come back?

由 一世执手 提交于 2019-12-04 17:56:24
I'm working on a stereoscopy application in C++ and OpenGL (for medical image visualization). From what I understand, the technology was quite big news about 10 years ago but it seems to have died down since. Now, many companies seem to be investing in the technology... Including nVidia it would seem . Stereoscopy is also known as "3D Stereo", primarily by nVidia (I think). Does anyone see stereoscopy as a major technology in terms of how we visualize things? I'm talking in both a recreational and professional capacity. With nVidia's 3D kit you don't need to "make a stereoscopy application",

How to calculate the fundamental matrix for stereo vision

由 ぃ、小莉子 提交于 2019-12-04 08:40:34
问题 I'm trying to write some code that will calculate the fundamental matrix to determine the relationship between stereo images. I started with the Hartley and Zisserman book that most people recommend, but it didn't have any practical examples and the sample code for it was in MATLAB which I don't have. I then switched to An introduction to 3D Computer Vision Techniques and Algorithms which is more practical and has actual examples in it. I implemented the recommended 8-point algorithm using
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