To find a list of planar patches in a point cloud, we can use detect_planar_patches. These planes can then be bounded using the 2D convex hull of their associated point sets and planar patches are extracted. After an initial set of planes are found, an iterative procedure is used to grow and merge planes into a smaller, stable set of planes. Spread of this distribution is too high, as measured using a coplanarity metric (see Fig. Second, the distribution of the distances from the fitted plane to each point is computed. If the spread of this distribution is too high (i.e., there is too much variance amongst all of the associated point normals), then the plane is rejected. First, the distribution of angles between each point normal and the fitted plane normal is found. The robust planarity check consists of two main components. Position and the median point normal and estimating a plane \(ax + by + cz + d = 0\). A plane is fitted to a subset of points by taking the median point If the plane passes a robust planarity test, then it is accepted. This algorithm first subdivides the point cloud into smaller chunks (using an octree), then attempts to fit a plane to each chunk. In addition to finding the single plane with the largest support, Open3D includes an algorithm which uses a robust statistics-based approach for planar patch detection.
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