Point clouds are discrete sets of points describing a hyper-surface in a certain dimension. The particular case of refers to real objects and surfaces such as a table, a chair or a part of landscape, where the coordinates are the familiar (X, Y, Z) spatial coordinates.

Point clouds are discrete sets of points describing a hyper-surface in a certain dimension. The particular case of refers to real objects and surfaces such as a table, a chair or a part of landscape, where the coordinates are the familiar (X, Y, Z) spatial coordinates.
Point cloud registration is an important task in the field of computer vision. Its goal is to align under one coordinate system data sets which describe the same hyper-surface sampled from different directions, distances and visual conditions.
In general, this task is divided to two parts:
1) Find for each point in a source point cloud (have to be aligned) her matching point in the target cloud. This is the correspondence problem. 2) Calculate the best transformation to align the source cloud with the target cloud according the correspondences (consider a cost e.g. MSE), and applying it on the source cloud. Our goal is to compare state of the art methods for registration between two 3-D point clouds as well as suggesting new algorithms and improvements. We focus on isolated and rigid objects. Thus, deformation is not handled and only translation and rotation transformations are allowed (we only deal with 6 DOFs – degrees of freedom which include rotations and translations with respect to X, Y, Z spatial axes).
Different registration methods, one from the literature and the other based on our original descriptor, were implemented in MATLAB. In addition, different correspondences filtering methods were tested for each algorithm. |