Real Scan Data Research Articles

Variational Bayesian noise estimation of point sets

Scanning devices acquire geometric information from the surface of an object in the form of a 3D point set. Such point sets, as any data obtained by means of physical measurement, contain some noise. To create an accurate model of the scanned object, this noise should be resolved before or during the process of surface reconstruction. In this paper, we develop a statistical technique to estimate the noise in a scanned point set. The noise is represented as normal distributions with zero mean and their variances determine the amount of the noise. These distributions are estimated with a variational Bayesian method, which is known to provide more robust estimations than point estimate methods, such as maximum likelihood and maximum a posteriori. Validation experiments and further tests with real scan data show that the proposed technique can accurately estimate the noise in a 3D point set.

A comparison of line extraction algorithms using 2D range data for indoor mobile robotics

This paper presents an experimental evaluation of different line extraction algorithms applied to 2D laser scans for indoor environments. Six popular algorithms in mobile robotics and computer vision are selected and tested. Real scan data collected from two office environments by using different platforms are used in the experiments in order to evaluate the algorithms. Several comparison criteria are proposed and discussed to highlight the advantages and drawbacks of each algorithm, including speed, complexity, correctness and precision. The results of the algorithms are compared with ground truth using standard statistical methods. An extended case study is performed to further evaluate the algorithms in a SLAM application.

A discriminative 3D wavelet-based descriptors: Application to the recognition of human body postures

This paper deals with the recognition of human body postures from a cloud of 3D points acquired by a human body scanner. Motivated by finding a representation that embodies a high power of discrimination between posture classes, a new type of 3D shape descriptors is suggested, namely wavelet transform coefficients (WC). These features can be seen as an extension to 3D of the 2D wavelet shape descriptors developed by (Shen, D., Ip, H.H.S., 1999. Pattern Recognition, 32, 151–165). The WC is compared with other 3D shape descriptors, within a Bayesian classification framework. Experiments with real scan data show that the WC outperforms other standard 3D shape descriptors in terms of discrimination power and classification rate.