Detection of clustered objects in sparse point clouds through 2D classification and quadric filtering

Abstract

A novel approach for detecting single objects in large clusters is presented. The proposed method is designed to work with structure from motion data, which typically includes a set of input images, a very sparse point cloud and camera poses. We use provided objects of interest from 2D classification, which are then projected to three dimensional space. The main contribution of this paper is an algorithm, which accurately detects the objects of interest and approximates their locations in three dimensional space, by using 2D classification data and quadric filtering. Optionally, a partly dense reconstructed mesh, containing objects of interest only, is computed, without the need for applying patch based multiple view stereo algorithms first. Experiments are performed on a challenging database containing images of wood log piles with a known ground truth number of objects, provided by timber processing companies. The average true positive rate exceeds 98.0% in every case, while it is shown how to reduce the false positive rate to less than 0.5%.