A Contrario 3D Point Alignment Detection Algorithm
Álvaro Gómez, Gregory Randall, Rafael Grompone von Gioi
⚠ This is a preprint. It may change before it is accepted for publication.


In this article we present an algorithm for the detection of perceptually relevant alignments in 3D point clouds. The algorithm is an extension of the algorithm developed by Lezama et al. for the case of sets of 2D points. The algorithm is based on the a contrario detection theory that mathematically formalizes the non-accidentalness principle proposed for perception: an observed structure is relevant if it would rarely occur by chance. This framework has been widely used in different detection tasks and leads to algorithms with a single critical parameter to control the number of false detections.