Out-of-Core Visualization of Classified 3D Point Clouds

Richter, Rico; Discher, Sören; Döllner, Jürgen

doi:10.1007/978-3-319-12181-9_14

Cited by 26 publications

(19 citation statements)

References 39 publications

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“…3D point clouds represent an essential category of geodata used in a variety of geoinformation applications and systems. Commonly, the interactive visualization of 3D point clouds is just as important as their efficient processing or analysis, for example, to effectively communicate analysis results (Richter et al, 2015) or to present digital twins of real-world sites to a larger public (Martinez-Rubi et al, 2016;Rüther et al, 2012). Our rendering system enables users to interactively explore arbitrary large 3D point clouds on consumer-level VR devices, providing immersion-preserving visual quality and frame rates that avoid motion sickness at all times.…”

Section: Discussionmentioning

confidence: 99%

“…A general overview of point-based rendering techniques is given by Gross and Pfister (2011). External memory algorithms as a means to render arbitrary large 3D point clouds were initially introduced by Rusinkiewicz and Levoy (2000) and have since been adopted by numerous authors (Martinez-Rubi et al, 2016;Richter et al, 2015). Visual optimization techniques for 3D point clouds aim to reduce overdraw and underdraw alike, either preventing such visual artifacts by rendering points with an appropriate size and orientation (Schütz and Wimmer, 2015;Preiner et al, 2012) or eliminating them via image-based post-processing (Dobrev et al, 2010;Rosenthal and Linsen, 2008).…”

Section: Related Workmentioning

confidence: 99%

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Interaction and Locomotion Techniques for the Exploration of Massive 3d Point Clouds in Vr Environments

Thiel

Discher

Richter

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 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 90fps instead of 30&ndash;60fps). 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.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Interaction and Locomotion Techniques for the Exploration of Massive 3d Point Clouds in Vr Environments

Thiel

Discher

Richter

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Instead, more effort has been concentrated on using established techniques in domains that require the visualization of large datasets as a tool for other purposes. For example, city visualization using aerial LIDAR [28], [29], sonar data visualization [30] and, more prominently, virtual reality [31]- [34].…”

Section: Related Workmentioning

confidence: 99%

Concomitant Hierarchy Construction and Rendering of Large Point Clouds

Silva

Marroquim

Esperança

2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

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Rendering large point clouds ordinarily requires building a hierarchical data structure for accessing the points that best represent the object for a given viewing frustum and level-of-detail. The building of such data structures frequently represents a large portion of the cost of the rendering pipeline both in terms of time and space complexity, especially when rendering is done for inspection purposes only. In this work we present OMiCroN -- Oblique Multipass Hierarchy Creation while Navigating -- which is the first algorithm capable of immediately displaying partial renders of the geometry, provided the cloud is made available sorted in Morton order. In fact, a pipeline coupling OMiCroN with an incremental sorting algorithm running in parallel can start rendering as soon as the first sorted prefix is produced, making this setup very convenient for streamed viewing.

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“…Crucial aspects are forming balanced tree representations with evenly-sized nodes to be able to swap nodes in memory. Based on a prior point labelling step visual integrity can be better maintained (Richter et al, 2015). In Wimmer and Scheiblauer (2006) a visualisation approach is presented without the need for expensive pre-processing steps.…”

Section: Point Cloud Visualization and Compressionmentioning

confidence: 99%

An IFC schema extension and binary serialization format to efficiently integrate point cloud data into building models

Krijnen

Beetz

2017

Advanced Engineering Informatics

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In this paper we suggest an extension to the Industry Foundation Classes (IFC) model to integrate point cloud datasets. The proposal includes a schema extension to the core model allowing the storage of points, either as Cartesian coordinates, points in parametric space of associated building element surfaces or as discrete height fields projected as grids onto building elements. To handle the considerable amounts of data generated in the process of scanning building structures, we present intelligent compression approaches combined with the Hierarchical Data Format (HDF) as an efficient serialization and an alternative to clear text encoded ISO 10303 part 21 files. Based on prototypical implementations we show results of various serialization options and their impacts on storage efficiency.In this proposal the deepened semantic relationships have been favoured over compression ratios. Nevertheless, with various near-lossless layers of compression and binary serialization applied, a compression ratio of up to 67.7% is obtained for a building model with integrated point clouds, compared to the raw source data. The binary serialization is able to handle hundreds of millions of points, out of which specific spatial and semantic subsets can rapidly be extracted due to the containerized hierarchical storage model and association to building elements. The authors advocate the use of binary storage for sizeable point cloud scans, but also show how especially the grid discretization can result into usable points cloud segments embedded into text-based IFC models.

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Out-of-Core Visualization of Classified 3D Point Clouds

Cited by 26 publications

References 39 publications

Interaction and Locomotion Techniques for the Exploration of Massive 3d Point Clouds in Vr Environments

Interaction and Locomotion Techniques for the Exploration of Massive 3d Point Clouds in Vr Environments

Concomitant Hierarchy Construction and Rendering of Large Point Clouds

An IFC schema extension and binary serialization format to efficiently integrate point cloud data into building models

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