point-clouds

Is there an official specification for the XYZ format for point clouds? I've been searching all over and I didn't find it. I've seen that there are some files which line contains: points coordinates, (X Y Z for each point ) others contain coordinates plus colors, (X Y Z R G B for each point ) there are even others that have an "Intensity" parameter. I need to consider all the possibilities. No, there is not an official specification about the .xyz format for point clouds. The .xyz format can be considered as part of a more general type of file formats: ASCII point cloud . You can consider

I'm trying to visualise point cloud using PCL CloudViewer. The problem is that I'm quite new to C++ and I have found two tutorials first demonstrating the creation of PointCloud and second demonstration the visualisation of a PointCloud. However, I'm not able to combine these two tutorials. Here is what I come with: #include <iostream> #include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/visualization/cloud_viewer.h> int main (int argc, char** argv) { pcl::PointCloud<pcl::PointXYZ> cloud; // Fill in the cloud data cloud.width = 5; cloud.height = 1; cloud.is_dense = false;

I have a set of point cloud forming a plane in 3D, which I've obtained from RANSAC plane fitting. For some specific kind of analysis of the data I need to convert this to a 2D problem so that I can have all the z-values almost same. Suppose the equation of the plane is ax+by+cz+1=0. My question is that: How can I get the values of a,b,c from raw point cloud data? Will least square be the best approach or there is any way to obtain these values from RANSAC fitting? From some tutorials I got an idea about doing the following steps: translate to centre of mass, rotate about x-axis, rotate about y

I have two point clouds, in 3d coordinates. One is a subset of the other, containing many less points. They are in the same scale. What i need to do is find the translation and rotation between the two. I have looked at Point cloud Library, "Iterative closest point" , and Coherent Point Drift , but these matching approaches both seem to expect the two point sets to contain mostly the same points, not have one be a smaller, subset of the other. Can i use either of these, with adjustments? Or is there another algorithm to match a subset point cloud to a set? Thank you. Without having access to

I need to find the transformation and rotation difference between two 3d point clouds. For this I am looking at PCL, as it seems ideal. On clean test data I have Iterative closest point working, but giving strange results(although I may have implemented it incorrectly...) I have pcl::estimateRigidTransformation working, and it seems better, although I assume will deal worse with noisy data. My question is: The two clouds will be noisy, and although they should contain the same points, there will be some discrepancies. What is the best way to deal with this? Should I find corresponding features

As in the title, I want to fit a cylinder to a group of 3D points with Python. This is a nice solution with MATLAB . How can we do it with Python? There is paper at David Eberly site "Fitting 3D Data with a Cylinder" that describes math basics and shows pseudocode. You can also refer to C++ code in Geometric Tools Engine at the same site. I think that some auxiliary math functions like matrix inverse etc could be implemented in NymPy. Using scipy.optimize.leastsq, we can create an error function in which the difference between the observed cylinder radius and the modelled radius is minimized.