Spatial grouping of 3D points from multiple stereovision sensors

This paper presents a method for grouping 3D points into cuboids. The 3D points are extracted using multiple stereovision sensors, and the sensor fusion module performs the fusion of the data sets and the grouping of the points in a single algorithm. The fusion/grouping algorithm is scalable, being able to work using any number of sensors, including a single one. The grouping method relies on a method of transforming the 3D space so that the density of the points is kept constant, and all the points belonging to a single object are adjacent, making the grouping of points into cuboids a simple labeling problem.