In this document, we describe the point cloud registration API and its modules: the estimation and rejection of point correspondences, and the estimation of rigid transformations.
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This tutorial gives an example of how to use the iterative closest point algorithm to see if one PointCloud is just a rigid transformation of another PointCloud.
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- TestCode : examples/official/Registration/iterative_closest_point.py
This document demonstrates using the Iterative Closest Point algorithm in order to incrementally register a series of point clouds two by two.
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This tutorial will teach you how to build an interactive ICP program
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This document demonstrates using the Normal Distributions Transform algorithm to register two large point clouds.
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- TestCode : examples/official/Registration/normal_distributions_transform.py
This document shows how to use the In-hand scanner applications to obtain colored models of small objects with RGB-D cameras.
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In this tutorial, we show how to find the alignment pose of a rigid object in a scene with clutter and occlusions.
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- TestCode : examples/official/Registration/alignment_prerejective.py