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The Point Cloud Library (PCL) is an open-source library of algorithms for point cloud processing tasks and 3D geometry processing, such as occur in three-dimensional computer vision. The library contains algorithms for filtering, feature estimation, surface reconstruction, 3D registration , [ 5 ] model fitting , object recognition , and ...
CloudCompare an open source point and model processing tool that includes an implementation of the ICP algorithm. Released under the GNU General Public License. PCL (Point Cloud Library) is an open-source framework for n-dimensional point clouds and 3D geometry processing. It includes several variants of the ICP algorithm.
A single ray of light from x (3D point) is dispersed in the lens system of the cameras according to a point spread function. The recovery of the corresponding image point from measurements of the dispersed intensity function in the images gives errors. In a digital camera, the image intensity function is only measured in discrete sensor elements.
Another type of feature recently made practical for structure from motion are general curves (e.g., locally an edge with gradients in one direction), part of a technology known as pointless SfM, [7] [8] useful when point features are insufficient, common in man-made environments. [9] The features detected from all the images will then be matched.
Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested.
While point clouds can be directly rendered and inspected, [10] [11] point clouds are often converted to polygon mesh or triangle mesh models, non-uniform rational B-spline (NURBS) surface models, or CAD models through a process commonly referred to as surface reconstruction. There are many techniques for converting a point cloud to a 3D ...
Inserting a new point into a balanced k-d tree takes O(log n) time. Removing a point from a balanced k-d tree takes O(log n) time. Querying an axis-parallel range in a balanced k-d tree takes O(n 1−1/k +m) time, where m is the number of the reported points, and k the dimension of the k-d tree.
The 3D point corresponding to a specific image point is constrained to be on the line of sight. From a single image, it is impossible to determine which point on this line corresponds to the image point. If two images are available, then the position of a 3D point can be found as the intersection of the two projection rays.