3d-reconstruction

How can I create minimal OOBB for given points? Creating AABB or sphere is very easy, but I have problems creating minimal OOBB. [edit] First answer didn't get me good results. I don't have huge cloud of points. I have little amount of points. I am doing collision geometry generation. For example, cube has 36 points (6 sides, 2 triangles each, 3 points for each triangle). And algorithm from first post gave bad results for cube. Example points for cube: http://nopaste.dk/download/3382 (should return identity axis) The PCA/covariance/eigenvector method essentially finds the axes of an ellipsoid

I am doing a project in opencv to detect obstacle in the path of a blind person using stereo calibration. I have calculated the disparity map correctly. Now to find the distance of obstacle from the camera, I want its 3D coordinates [X,Y,Z] , which I am guessing can be found by reprojectImageTo3D(), but I dont have the Q matrix to use in this function because the Q matrix I am getting from stereoRectify() is coming null probably because I used pre calibrated images. Although I do have the inrinsic and extrinsic parameters of my camera. So my question is that how can I manually create the Q

I'm trying to calculate a new camera position based on the motion of corresponding images. the images conform to the pinhole camera model. As a matter of fact, I don't get useful results, so I try to describe my procedure and hope that somebody can help me. I match the features of the corresponding images with SIFT, match them with OpenCV's FlannBasedMatcher and calculate the fundamental matrix with OpenCV's findFundamentalMat (method RANSAC). Then I calculate the essential matrix by the camera intrinsic matrix (K): Mat E = K.t() * F * K; I decompose the essential matrix to rotation and

I'm new in this field and I'm trying to model a simple scene in 3d out of 2d images and I dont have any info about cameras. I know that there are 3 options : I have two images and I know the model of my camera (intrisics) that I loaded from a XML for instance loadXMLFromFile() => stereoRectify() => reprojectImageTo3D() I don't have them but I can calibrate my camera => stereoCalibrate() => stereoRectify() => reprojectImageTo3D() I can't calibrate the camera (it is my case, because I don't have the camera that has taken the 2 images, then I need to find pair keypoints on both images with SURF,

If I take a picture with a camera, so I know the distance from the camera to the object, such as a scale model of a house, I would like to turn this into a 3D model that I can maneuver around so I can comment on different parts of the house. If I sit down and think about taking more than one picture, labeling direction, and distance, I should be able to figure out how to do this, but, I thought I would ask if someone has some paper that may help explain more. What language you explain in doesn't matter, as I am looking for the best approach. Right now I am considering showing the house, then

I am working in Matlab and I have a 3d matrix with dimensions 384x512x160, which is made of 384x512 slices. How can I plot data like that? What you really have is Volumetric data . I guess that you have is a value of C for every X,Y,Z. And it actually depends a lot in what type of data you have. You'd need to give more information for an specific answer. In general have a look to Volumetric data visualization techniques, but there is not just one way of doing this. Following i will leave you some examples you may want to try. Slideomatic One option is to use the Sliceomatic from FE: Plotting