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In computer science, jump point search (JPS) is an optimization to the A* search algorithm for uniform-cost grids. It reduces symmetries in the search procedure by means of graph pruning, [1] eliminating certain nodes in the grid based on assumptions that can be made about the current node's neighbors, as long as certain conditions relating to the grid are satisfied.
The advantage is that all optimizations of grid A* like jump point search will apply. A visibility graph with all the grid points can be searched with A* for the optimal solution in 2D space. However, the performance is problematic since the number of edges in a graph with V {\displaystyle V} vertices is O ( V 2 ) {\displaystyle O(V^{2})} .
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 ...
A point cloud image of a torus Geo-referenced point cloud of Red Rocks, Colorado (by DroneMapper) A point cloud is a discrete set of data points in space. The points may represent a 3D shape or object. Each point position has its set of Cartesian coordinates (X, Y, Z).
A view frustum The appearance of an object in a pyramid of vision When creating a parallel projection, the viewing frustum is shaped like a box as opposed to a pyramid.. In 3D computer graphics, a viewing frustum [1] or view frustum [2] is the region of space in the modeled world that may appear on the screen; it is the field of view of a perspective virtual camera system.
Princeton shape-based 3D model search engine; Keenan's 3D Model Repository hosted by the Carnegie Mellon University; HeiCuBeDa Hilprecht – Heidelberg Cuneiform Benchmark Dataset for the Hilprecht Collection a collection of almost 2.000 cuneiform tablets for bulk-download acquired with a high-resolution 3D-scanner.
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Point set registration is the process of aligning two point sets. Here, the blue fish is being registered to the red fish. In computer vision, pattern recognition, and robotics, point-set registration, also known as point-cloud registration or scan matching, is the process of finding a spatial transformation (e.g., scaling, rotation and translation) that aligns two point clouds.