Abstract: A feature detector and a feature descriptor are presented, which are applicable to 3D range data. The feature detector is used to identify locations in the range data at which the feature descriptor is applied. The feature descriptor, or feature transform, calculates a signature for each identified location on the basis of local shape information. The approach used in both the feature detector and the descriptor is motivated by the success of the scale invariant feature transform and speeded up robust features approaches in the 2D case. Using synthetic data, the authors evaluate the repeatability of the detector and robustness of the descriptor to global transformations and image noise. The complete system is then applied to the problem of automatic detection of repeated structure in real range images.
REF introduced a THRIFT feature by calculating a weighted histogram of the deviation angles between the normals of the neighboring points and the feature point.
Local 3D structure recognition in range images
Iet Computer Vision
Mathematics