Rigid point cloud registration plays an important role in self-driving, drones, robotics, and many other fields. The sensor limitations and external environment interference in practical applications lead to low overlap in a pair of point clouds which have some problems such as missing points and extraneous points. However, most of the current registration methods rely on complete point clouds to compute the correspondences, showing an unsatisfactory performance in low-overlap point cloud registration. In this letter, we present a voxel prelocalization multiscale relocalization block feature-based (VMB) overlap region extraction module which enables us to solve this problem. In this module, the key points for point cloud segmentation are located using voxel downsampling and relocated based on a weighted sum of multiscale normal angles. Then, the high-overlap point cloud blocks are extracted by matching three-point-combination-based feature of each block. Experiments on low-overlap datasets demonstrate the effectiveness and robustness of our method comparing with the other competing methods.

Although privacy leaking through content analysis of Wi-Fi traffic has received an increased attention, privacy inference through meta-data (e.g. IP, Host) analysis of Wi-Fi traffic represents a potentially more serious threat to user privacy. Firstly, it represents a more efficient and scalable approach to infer users' sensitive information without checking the content of Wi-Fi traffic. Secondly, meta-data based demographics inference can work on both unencrypted and encrypted traffic (e.g., HTTPS traffic). In this study, we present a novel approach to infer user demographic information by exploiting the meta-data of Wi-Fi traffic. We develop a proof-of-concept prototype, Demographic Information Predictor (DIP) system, and evaluate its performance on a real-world dataset, which includes the Wi-Fi access of 28,158 users in 5 months. DIP extracts four kinds of features from real-world Wi-Fi traffic and proposes a novel machine learning based inference technique to predict user demographics. Our analytical results show that, for unencrypted traffic, DIP can predict gender and education level of users with an accuracy of 78% and 74% respectively. It is surprising to show that, even for HTTPS traffic, user demographics can still be predicted at a precision of 67% and 72% respectively, which well demonstrates the practicality of the proposed privacy inference scheme.