Analysis of Interactive Editing Operations for Out-of-Core Point-Cloud Hierarchies

In this paper we compare the time and space complexity of editing operations on two data structures which are suitable for visualizing huge point clouds. The first data structure was introduced by Scheiblauer andWimmer [SW11] and uses only the original points from a source data set for building a level-of-detail hierarchy that can be used for rendering points clouds. The second data structure introduced byWand et al. [WBB+07] requires additional points for the level-of-detail hierarchy and therefore needs more memory when stored on disk. Both data structures are based on an octree hierarchy and allow for deleting and inserting points. Besides analyzing and comparing these two data structures we also introduce an improvement to the points deleting algorithm for the data structure of Wand et al. [WBB+07], which thus allows for a more efficient node loading strategy during rendering

Large-scale point-cloud visualization through localized textured surface reconstruction

In this paper, we introduce a novel scene representation for the visualization of large-scale point clouds accompanied by a set of high-resolution photographs. Many real-world applications deal with very densely sampled point-cloud data, which are augmented with photographs that often reveal lighting variations and inaccuracies in registration. Consequently, the high-quality representation of the captured data, i.e., both point clouds and photographs together, is a challenging and time-consuming task. We propose a two-phase approach, in which the first (preprocessing) phase generates multiple overlapping surface patches and handles the problem of seamless texture generation locally for each patch. The second phase stitches these patches at render-time to produce a high-quality visualization of the data. As a result of the proposed localization of the global texturing problem, our algorithm is more than an order of magnitude faster than equivalent mesh-based texturing techniques. Furthermore, since our preprocessing phase requires only a minor fraction of the whole data set at once, we provide maximum flexibility when dealing with growing data sets

Interactions with gigantic point clouds

Zusammenfassung in deutscher SpracheDuring the last decade the increased use of laser range-scanners for sampling the environment has led to gigantic point cloud data sets. Due to the size of such data sets, tasks like viewing, editing, or presenting the data have become a challenge per se, as the point data is too large to fit completely into the main memory of a customary computer system. In order to accomplish these tasks and enable the interaction with gigantic point clouds on consumer grade computer systems, this thesis presents novel methods and data structures for efficiently dealing with point cloud data sets consisting of more than 109 point samples. To be able to access point samples fast that are stored on disk or in memory, they have to be spatially ordered, and for this a data structure is proposed which organizes the points samples in a level-of-detail hierarchy. Point samples stored in this hierarchy cannot only be rendered fast, but can also be edited, for example existing points can be deleted from the hierarchy or new points can be inserted. Furthermore, the data structure is memory efficient, as it only uses the point samples from the original data set. Therefore, the memory consumption of the point samples on disk, when stored in this data structure, is comparable to the original data set. A second data structure is proposed for selecting points. This data structure describes a volume inside which point samples are considered to be selected, and this has the advantage that the information about a selection does not have to be stored at the point samples. In addition to these two previously mentioned data structures, which represent novel contributions for point data visualization and manipulation, methods for supporting the presentation of point data sets are proposed. With these methods the user experience can be enhanced when navigating through the data. One possibility to do this is by using regional meshes that employ an out-of-core texturing method to show details in the mesoscopic scale on the surface of sampled objects, and which are displayed together with point clouds. Another possibility to increase the user experience is to use graphs in 3D space, which helps users to orient themselves inside point cloud models of large sites, where otherwise it would be difficult to find the places of interest. Furthermore, the quality of the displayed point cloud models can be increased by using a point size heuristics that can mimic a closed surface in areas that would otherwise appear undersampled, by utilizing the density of the rendered points in the different areas of the point cloud model. Finally, the use of point cloud models as a tool for archaeological work is proposed. Since it becomes increasingly common to document archaeologically interesting monuments with laser scanners, the number application areas of the resulting point clouds is raising as well. These include, but are not limited to, new views of the monument that are impossible when studying the monument on-site, creating cuts and floor plans, or perform virtual anastylosis. All these previously mentioned methods and data structures are implemented in a single software application that has been developed during the course of this thesis and can be used to interactively explore gigantic point clouds.18

Analysis of Interactive Editing Operations for Out-of-Core Point-Cloud Hierarchies

In this paper we compare the time and space complexity of editing operations on two data structures which are suitable for visualizing huge point clouds. The first data structure was introduced by Scheiblauer andWimmer [SW11] and uses only the original points from a source data set for building a level-of-detail hierarchy that can be used for rendering points clouds. The second data structure introduced byWand et al. [WBB+07] requires additional points for the level-of-detail hierarchy and therefore needs more memory when stored on disk. Both data structures are based on an octree hierarchy and allow for deleting and inserting points. Besides analyzing and comparing these two data structures we also introduce an improvement to the points deleting algorithm for the data structure of Wand et al. [WBB+07], which thus allows for a more efficient node loading strategy during rendering

Instant Points: Fast Rendering of Unprocessed Point Clouds

We present an algorithm to display enormous unprocessed point clouds at interactive rates without requiring long postprocessing. The novelty here is that we do not make any assumptions about sampling density or availability of normal vectors for the points. This is very important because such information is available only after lengthy postprocessing of scanned datasets, whereas users want to interact with the dataset immediately. Instant Points is an out-of-core algorithm that makes use of nested octrees and an enhanced version of sequential point trees. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Display algorithms, Viewing algorithm