TY  - JOUR
A1  - Richter, Rico
A1  - Döllner, Jürgen Roland Friedrich
T1  - Concepts and techniques for integration, analysis and visualization of massive 3D point clouds
JF  - Computers, environment and urban systems
N2  - Remote sensing methods, such as LiDAR and image-based photogrammetry, are established approaches for capturing the physical world. Professional and low-cost scanning devices are capable of generating dense 3D point clouds. Typically, these 3D point clouds are preprocessed by GIS and are then used as input data in a variety of applications such as urban planning, environmental monitoring, disaster management, and simulation. The availability of area-wide 3D point clouds will drastically increase in the future due to the availability of novel capturing methods (e.g., driver assistance systems) and low-cost scanning devices. Applications, systems, and workflows will therefore face large collections of redundant, up-to-date 3D point clouds and have to cope with massive amounts of data. Hence, approaches are required that will efficiently integrate, update, manage, analyze, and visualize 3D point clouds. In this paper, we define requirements for a system infrastructure that enables the integration of 3D point clouds from heterogeneous capturing devices and different timestamps. Change detection and update strategies for 3D point clouds are presented that reduce storage requirements and offer new insights for analysis purposes. We also present an approach that attributes 3D point clouds with semantic information (e.g., object class category information), which enables more effective data processing, analysis, and visualization. Out-of-core real-time rendering techniques then allow for an interactive exploration of the entire 3D point cloud and the corresponding analysis results. Web-based visualization services are utilized to make 3D point clouds available to a large community. The proposed concepts and techniques are designed to establish 3D point clouds as base datasets, as well as rendering primitives for analysis and visualization tasks, which allow operations to be performed directly on the point data. Finally, we evaluate the presented system, report on its applications, and discuss further research challenges.
KW  - 3D point clouds
KW  - System architecture
KW  - Classification
KW  - Out-of-core
KW  - Visualization
Y1  - 2014
U6  - https://doi.org/10.1016/j.compenvurbsys.2013.07.004
SN  - 0198-9715
SN  - 1873-7587
VL  - 45
SP  - 114
EP  - 124
PB  - Elsevier
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Westphal, Florian
A1  - Axelsson, Stefan
A1  - Neuhaus, Christian
A1  - Polze, Andreas
T1  - VMI-PL: A monitoring language for virtual platforms using virtual machine introspection
JF  - Digital Investigation : the international journal of digital forensics & incident response
N2  - With the growth of virtualization and cloud computing, more and more forensic investigations rely on being able to perform live forensics on a virtual machine using virtual machine introspection (VMI). Inspecting a virtual machine through its hypervisor enables investigation without risking contamination of the evidence, crashing the computer, etc. To further access to these techniques for the investigator/researcher we have developed a new VMI monitoring language. This language is based on a review of the most commonly used VMI-techniques to date, and it enables the user to monitor the virtual machine's memory, events and data streams. A prototype implementation of our monitoring system was implemented in KVM, though implementation on any hypervisor that uses the common x86 virtualization hardware assistance support should be straightforward. Our prototype outperforms the proprietary VMWare VProbes in many cases, with a maximum performance loss of 18% for a realistic test case, which we consider acceptable. Our implementation is freely available under a liberal software distribution license. (C) 2014 Digital Forensics Research Workshop. Published by Elsevier Ltd. All rights reserved.
KW  - Virtualization
KW  - Security
KW  - Monitoring language
KW  - Live forensics
KW  - Introspection
KW  - Classification
Y1  - 2014
U6  - https://doi.org/10.1016/j.diin.2014.05.016
SN  - 1742-2876
SN  - 1873-202X
VL  - 11
SP  - S85
EP  - S94
PB  - Elsevier
CY  - Oxford
ER  -