INTERACTION AND LOCOMOTION TECHNIQUES FOR THE EXPLORATION OF MASSIVE 3D POINT CLOUDS IN VR ENVIRONMENTS

<p><strong>Abstract.</strong> Emerging virtual reality (VR) technology allows immersively exploring digital 3D content on standard consumer hardware. Using in-situ or remote sensing technology, such content can be automatically derived from real-world sites. External memory algorithms allow for the non-immersive exploration of the resulting 3D point clouds on a diverse set of devices with vastly different rendering capabilities. Applications for VR environments raise additional challenges for those algorithms as they are highly sensitive towards visual artifacts that are typical for point cloud depictions (i.e., overdraw and underdraw), while simultaneously requiring higher frame rates (i.e., around 90<span class="thinspace"></span>fps instead of 30&amp;ndash;60<span class="thinspace"></span>fps). We present a rendering system for the immersive exploration and inspection of massive 3D point clouds on state-of-the-art VR devices. Based on a multi-pass rendering pipeline, we combine point-based and image-based rendering techniques to simultaneously improve the rendering performance and the visual quality. A set of interaction and locomotion techniques allows users to inspect a 3D point cloud in detail, for example by measuring distances and areas or by scaling and rotating visualized data sets. All rendering, interaction and locomotion techniques can be selected and configured dynamically, allowing to adapt the rendering system to different use cases. Tests on data sets with up to 2.6 billion points show the feasibility and scalability of our approach.</p>

Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute

We consider privacy-preserving computation of big data using trusted computing primitives with limited private memory. Simply ensuring that the data remains encrypted outside the trusted computing environment is insufficient to preserve data privacy, for data movement observed during computation could leak information. While it is possible to thwart such leakage using generic solution such as ORAM [42], designing efficient privacy-preserving algorithms is challenging. Besides computation efficiency, it is critical to keep trusted code bases lean, for large ones are unwieldy to vet and verify. In this paper, we advocate a simple approach wherein many basic algorithms (e.g., sorting) can be made privacy-preserving by adding a step that securely scrambles the data before feeding it to the original algorithms. We call this approach Scramble-then-Compute (StC), and give a sufficient condition whereby existing external memory algorithms can be made privacy-preserving via StC. This approach facilitates code-reuse, and its simplicity contributes to a smaller trusted code base. It is also general, allowing algorithm designers to leverage an extensive body of known efficient algorithms for better performance. Our experiments show that StC could offer up to 4.1× speedups over known, application-specific alternatives.

Disk-Based Search

The need to deal with large data sets is at the heart of many real-world problems. In many organizations the data size has already surpassed Petabytes (1015). It is clear that to process such an enormous amount of data, the physical limitations of RAM is a major hurdle. However, the media that can hold huge data sets, i.e., hard disks, are about a 10,000 to 1,000,000 times slower to access than RAM. On the other hand, the costs for large amounts of disk space have considerably decreased. This growing disparity has led to a rising attention to the design of external memory algorithms (Sanders et al., 2003) in recent years. In a hard disk, random disk accesses are slow due to disk latency in moving the head on top of the data. But once the head is at its proper position, data can be read very rapidly. External memory algorithms exploit this fact by processing the data in the form of blocks. They are more informed about the future accesses to the data and can organize their execution to have minimum number of block accesses. Traditional graph search algorithms perform well as long as the graph can fit into the RAM. But for large graphs these algorithms are destined to fail. In the following, we will review some of the advances in the field of search algorithms designed for large graphs.