An Improved Local Descriptor based Object Recognition in Cluttered 3D Point Clouds

Object recognition in three-dimensional point clouds is a new research topic in the field of computer vision. Numerous nuisances, such as noise, a varying density, and occlusion greatly increase the difficulty of 3D object recognition. An improved local feature descriptor is proposed to address these problems in this paper. At each feature point, a local reference frame is established by calculating a scatter matrix based on the geometric center and the weighted point-cloud density of its neighborhood, and an improved normal vector estimation method is used to generate a new signature of histograms of orientations (SHOT) local-feature descriptor. The geometric consistency and iterative closest point method realize 3D model recognition in the point-cloud scenes. The experimental results show that the proposed SHOT feature-extraction algorithm has high robustness and descriptiveness in the object recognition of 3D local descriptors in cluttered point-cloud scenes

Development of a system for point cloud based object recognition

In this thesis we address the problem of point cloud based 3D object recognition. First we develop the system with different 3D shape descriptors and then make a thorough analysis of results gathered from testing the system on a publicly available benchmark database. We also evaluate how dimensionality reduction and different methods for feature point selection influence on classification accuracy. The system is compared with the system for object recognition in 2D. We evaluate the system on different number of objects, including on objects that are very similar by shape. Based on results from the dataset, we develop a real-time system for object recognition. The system allows us to build our own database of objects that we want to recognize. We constructed a database of 18 objects on which we evaluated the system. Results presented in this thesis have shown that we can recognize objects sufficiently good only with 3D shape information