A Simple Semantic-Based Data Storage Layout for Querying Point Clouds

El-Mahgary, Sami; Virtanen, Juho‐Pekka; Hyyppä, Hannu

doi:10.3390/ijgi9020072

Cited by 10 publications

(4 citation statements)

References 63 publications

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“…These data amounts present a technical challenge and require suitable storage systems to be applied in processing and distribution. Understandably, point cloud storage [ 39 ], distribution [ 40 ], and application [ 41 ] have become topical development tasks.…”

Section: Discussion and Conclusionmentioning

confidence: 99%

Utilizing a Terrestrial Laser Scanner for 3D Luminance Measurement of Indoor Environments

et al. 2021

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We aim to present a method to measure 3D luminance point clouds by applying the integrated high dynamic range (HDR) panoramic camera system of a terrestrial laser scanning (TLS) instrument for performing luminance measurements simultaneously with laser scanning. We present the luminance calibration of a laser scanner and assess the accuracy, color measurement properties, and dynamic range of luminance measurement achieved in the laboratory environment. In addition, we demonstrate the 3D luminance measuring process through a case study with a luminance-calibrated laser scanner. The presented method can be utilized directly as the luminance data source. A terrestrial laser scanner can be prepared, characterized, and calibrated to apply it to the simultaneous measurement of both geometry and luminance. We discuss the state and limitations of contemporary TLS technology for luminance measuring.

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Section: Discussion and Conclusionmentioning

confidence: 99%

Utilizing a Terrestrial Laser Scanner for 3D Luminance Measurement of Indoor Environments

et al. 2021

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“…In a lower abstraction level L1-2, "Spaces" and "ConnectedElements" are connected to "Sub-spaces" and "WorldObjects", that constitute the entry points on which different domain ontologies / specializations can be integrated in level-2 (Poux et al, 2017), (Poux et al, 2018). (El-Mahgary et al, 2020) present a concept for splitting semantic and geometric information of point cloud data using two separate files (called "Points" and "Semantics"). Both files have exactly the same ordering of points and corresponding semantic information and, thus, do not need indexing.…”

Section: Related Work On Extending the Semantic Capabilities Of Point Cloudsmentioning

confidence: 99%

Integration of 3d Point Clouds With Semantic 3d City Models – Providing Semantic Information Beyond Classification

Beil

Kutzner

Schwab

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. A range of different and increasingly accessible acquisition methods, the possibility for frequent data updates of large areas, and a simple data structure are some of the reasons for the popularity of three-dimensional (3D) point cloud data. While there are multiple techniques for segmenting and classifying point clouds, capabilities of common data formats such as LAS for providing semantic information are mostly limited to assigning points to a certain category (classification). However, several fields of application, such as digital urban twins used for simulations and analyses, require more detailed semantic knowledge. This can be provided by semantic 3D city models containing hierarchically structured semantic and spatial information. Although semantic models are often reconstructed from point clouds, they are usually geometrically less accurate due to generalization processes. First, point cloud data structures / formats are discussed with respect to their semantic capabilities. Then, a new approach for integrating point clouds with semantic 3D city models is presented, consequently combining respective advantages of both data types. In addition to elaborate (and established) semantic concepts for several thematic areas, the new version 3.0 of the international Open Geospatial Consortium (OGC) standard CityGML also provides a PointCloud module. In this paper a scheme is shown, how CityGML 3.0 can be used to provide semantic structures for point clouds (directly or stored in a separate LAS file). Methods and metrics to automatically assign points to corresponding Level of Detail (LoD)2 or LoD3 models are presented. Subsequently, dataset examples implementing these concepts are provided for download.

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“…The major challenge with the data storage layout is inefficiency in data parsing. In one of the recent works, a simple data layout that used the semantics to allow quick queries from large point cloud data was presented (El-Mahgary et al, 2020). Algorithmically, an indexbased approach for storage and fast querying was explored and semantic data was used to partition the point cloud.…”

Section: Introductionmentioning

confidence: 99%

Metamorphism of Als Point Data for Multitude Application

Vijaywargiya,

Ramiya

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Technologically assisted decision-making in urban planning and governance is significant to envisage Sustainable Development Goal (SDG) 11, of developing sustainable cities and communities. In the current millennium, planners and decision-makers require knowledge-rich virtual models for managing the man-made and natural resources in the city. To effectively utilize the technological advancement for sustainable urban development, there is a need for expeditious entail towards accurate urban resource mapping, development of flexible monitoring and information extraction framework, and enticing visualization. Airborne LiDAR Scanning (ALS) is capable of producing very precise 3D geometric data over expansive urban areas in a timely and economically efficient manner. However, it is challenging to derive viable outcomes from the unstructured and voluminous point cloud. This work proposes an intermediary metamorphosed point cloud storage framework to enhance the utility of point clouds for multiple pragmatic applications. The proposed methodological approach transforms the unstructured, massive point cloud into an ontologically stored urban object collection to utilize the large-scale urban point cloud in decision-making. Further, the paper demonstrates the direct applicability of the metamorphosed point cloud storage framework for two specific applications related to sustainable urban development. Experiments carried out using the proposed framework on DALES benchmark dataset show promising results.

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A Simple Semantic-Based Data Storage Layout for Querying Point Clouds

Cited by 10 publications

References 63 publications

Utilizing a Terrestrial Laser Scanner for 3D Luminance Measurement of Indoor Environments

Utilizing a Terrestrial Laser Scanner for 3D Luminance Measurement of Indoor Environments

Integration of 3d Point Clouds With Semantic 3d City Models – Providing Semantic Information Beyond Classification

Metamorphism of Als Point Data for Multitude Application

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