External memory management and simplification of huge meshes

Cignoni, Paolo; Montani, Claudio; Rocchini, Claudio; Scopigno, Roberto

doi:10.1109/tvcg.2003.1260746

Cited by 127 publications

(65 citation statements)

References 24 publications

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“…Beside this application, also processing on the data is performed -for example Poisson surface reconstruction like in [Bolitho et al, 2007] or mesh simplification [Cignoni et al, 2003]. …”

Section: Out-of-corementioning

confidence: 99%

Filtering of Point Clouds from Photogrammetric Surface Reconstruction

Wenzel

Rothermel

Fritsch

et al. 2014

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

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ABSTRACT:The density and data volumes for recorded 3D surfaces increase steadily. In particular during photogrammetric surface reconstruction and laser scanning applications these volumes often exceed the limits of the available hardware and software. The large point clouds and meshes acquired during the projects contain billions of vertices and require scalable data handling frameworks for further processing. Beside the scalability to big data, these methods also should adapt to non-uniform data density and precision resulting from varying acquisition distances, as required for data from Photogrammetry and Laser Scanning. For this purpose, we present a framework called Pine Tree, which is based on an out-of-core octree. It enables fast local data queries, such as nearest neighbor queries for filtering, while dynamically storing and loading data from the hard disk. This way, large amounts of data can be processed on limited main memory. Within this paper, we describe the Pine Tree approach as well as its underlying methods. Furthermore, examples for a filtering task are shown, where overlapping point clouds are thinned out by preserving the locally densest point cloud only. By adding an optional redundancy constraint, point validation and outlier rejection can be applied.

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“…Beside this application, also processing on the data is performed -for example Poisson surface reconstruction like in [Bolitho et al, 2007] or mesh simplification [Cignoni et al, 2003]. …”

Section: Out-of-corementioning

confidence: 99%

Filtering of Point Clouds from Photogrammetric Surface Reconstruction

Wenzel

Rothermel

Fritsch

et al. 2014

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

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“…The basic common structures are trees (including B-trees [22] and its derivatives [23]). Cignoni et al [7] proposed an adapted a version of the octree [20], [21] devoted to the generic Out-of-Core algorithms for meshes that they called Octree Based External Memory Mesh. This structure is based on a decomposition of the cube including the triangulation recursively until the desired size of the elementary cubes (in number of vertices per cube, which are indexed locally in the cube) is reached.…”

Section: Triangle Soupmentioning

confidence: 99%

Modified Streaming Format for Direct Access Triangular Data Structures

Abid¹,

Mebarki²,

Hidouci³

2014

IJIGSP

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-We define in this paper an extended solution to improve an Out-of-Core data structure which is the streaming format, by adding new information allowing to reduce file access cost, reducing the neighborhood access delay to constant time.The original streaming format is conceived to manipulate huge triangular meshes. It assumes that the whole mesh cannot be loaded entirely into the main memory. That's why the authors did not include the neighborhood in the file structure.However, almost all of the applications need the neighborhood information in the triangular structures. Using the original streaming format does not allow us to extract the neighborhood information easily. By adding the neighbor indices to the file in the same way as the original format, we can benefit from the streaming format, and at the same time, guarantee a constant time access to the neighborhood.We have adapted our new structure so that it can allow us to apply our direct access algorithm to different parts of the structure without having to go through the entire file.

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“…Algorithms have been designed that guarantee the optimal number of disk I/O operations for specific problems [2,3], at the cost of greater programming complexity. Data layouts have been proposed that are I/O-efficient when accessed with sufficient locality [4,5], at the cost of an expensive initial re-ordering step. Instead, we advocate a streaming paradigm for processing large data sets, wherein the order in which computations are performed is dictated by the data's order [6,7].…”

Section: Introductionmentioning

confidence: 99%

Generating Raster DEM from Mass Points Via TIN Streaming

Isenburg

Liu

Shewchuk

et al. 2006

Geographic Information Science

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Abstract. It is difficult to generate raster Digital Elevation Models (DEMs) from terrain mass point data sets too large to fit into memory, such as those obtained by LIDAR. We describe prototype tools for streaming DEM generation that use memory and disk I/O very efficiently. From 500 million bare-earth LIDAR double precision points (11.2 GB) our tool can, in just over an hour on a standard laptop with two hard drives, produce a 50,394 × 30,500 raster DEM with 20 foot post spacing in 16 bit binary BIL format (3 GB), using less than 100 MB of main memory and less than 300 MB of temporary disk space.

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External memory management and simplification of huge meshes

Cited by 127 publications

References 24 publications

Filtering of Point Clouds from Photogrammetric Surface Reconstruction

Filtering of Point Clouds from Photogrammetric Surface Reconstruction

Modified Streaming Format for Direct Access Triangular Data Structures

Generating Raster DEM from Mass Points Via TIN Streaming

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