Just to give you a little more information to get started (if you choose to go down this path). I often use it as a reference for my own work. However, OpenSfM is written in Python and I think it is easy to navigate and understand. Note that these systems can become quite complicated. Please suggest me if above method is correct or how can i merge multiple 3d point clouds to construct a single 3-d structure.Īnother possible path of understanding for you would be to look at an open source implementation of structure from motion or SLAM. Points3d = np.concatenate((points3d, tripoints3d), 1)įig.suptitle('3D reconstructed', fontsize=16)Īx.plot(points3d, points3d, points3d, 'b.')īut I am getting very unexpected result. Tripoints3d = structure.linear_triangulation(points1n, points2n, P1, P2) #tripoints3d = structure.reconstruct_points(points1n, points2n, P1, P2) P2_homogenous = np.linalg.inv(np.vstack(])) # Convert P2 from camera view to world view # Using the essential matrix returns 4 possible camera paramters # Given we are at camera 1, calculate the parameters for camera 2 Print('Computed essential matrix:', (-E / E)) Points2n = np.dot(np.linalg.inv(intrinsic), points2)Į = pute_essential_normalized(points1n, points2n) Points1n = np.dot(np.linalg.inv(intrinsic), points1) # Calculate essential matrix with 2d points. Points1, points2, intrinsic = dino(files, files) #and outputs the keypoint point matches(corresponding points in two different views) along the camera intrinsic parameters. Pts1, pts2 = features.find_correspondence_points(img1, img2)Īx.imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))Īx.imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
#Make 3d model from 2d image code
With a few modification in the example.py code I tried to run this example on all the consecutive image pairs and merge the 3-d point clouds for 3d reconstruction of object ( dino) as below: import matplotlib.pyplot as plt Given we are at camera 1, calculate the parameters for camera 2 Using the essential matrix returns 4 possible camera parametersĮventually we use corresponding points and both camera parameters for 3d point estimation using triangulation method.Īfter going through theory section, as my first experiment I tried to run the code available here, We find the corresponding points in the two images using methods like SIFT or SURF etc.Īfter getting corresponding key point, we find find the essential matrix (say K) using minimum 8 key points (used in 8-point algorithm) What I have understood so far can be summarized as below:įor 3d point (depth map) reconstruction, we need 2 images of the same object from 2 different view, given such image pair we also need Camera matrix (say P1, P2) I am trying understand basics of 3d point reconstruction from 2d stereo images.
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