A Smart Point Cloud Infrastructure for intelligent environments

Poux, Florent; Billen, Roland

doi:10.1201/9781351018869-9

Cited by 22 publications

(29 citation statements)

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“…In this area, the work of Dietenbeck et al [6] makes use of multi-layer ontologies for integrating domain knowledge in the process of 3D shape segmentation and annotation. While they provide only an example and a manual approach for meshes, they describe an expert knowledge system for furniture in three conceptual layers, directly compatible with the three meta-models of the Smart Point Cloud Infrastructure introduced in [31] and extended in [29]. The first layer corresponds to the basic properties of any object, such as shapes and structures, whereas the upper layers are specific to each application domain and describe the functionalities and possible configurations of the objects in this domain.…”

Section: Knowledge Integration (Ki) For Object-relationship Modellingmentioning

confidence: 99%

“…Indoor 3D point clouds that host semantic information such as segments and classes are the starting point of our methodology for generating semantic models, and give insight into the morphology and geometry of building's interiors. For this purpose, we leverage the flexibility given by the Smart Point Cloud (SPC) Infrastructure [29] to consider point cloud data at different granularity levels. It permits to reason solely spatially, semantically, but also includes functionality description and descriptor characterization following the four levels of the Tower of Knowledge concept [107].…”

Section: Knowledge-base Structurationmentioning

confidence: 99%

“…Our approach addresses Knowledge Extraction (KE), Knowledge Integration (KI) and Knowledge Representation (KR) [29] to better assimilate point cloud data of various quality [30]. Indeed, most software and tools existing in our computerized environment were developed to work primarily with 3D models.…”

Section: Introductionmentioning

confidence: 99%

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3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

et al. 2018

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3D models derived from point clouds are useful in various shapes to optimize the trade-off between precision and geometric complexity. They are defined at different granularity levels according to each indoor situation. In this article, we present an integrated 3D semantic reconstruction framework that leverages segmented point cloud data and domain ontologies. Our approach follows a part-to-whole conception which models a point cloud in parametric elements usable per instance and aggregated to obtain a global 3D model. We first extract analytic features, object relationships and contextual information to permit better object characterization. Then, we propose a multi-representation modelling mechanism augmented by automatic recognition and fitting from the 3D library ModelNet10 to provide the best candidates for several 3D scans of furniture. Finally, we combine every element to obtain a consistent indoor hybrid 3D model. The method allows a wide range of applications from interior navigation to virtual stores.

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Section: Knowledge Integration (Ki) For Object-relationship Modellingmentioning

confidence: 99%

Section: Knowledge-base Structurationmentioning

confidence: 99%

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3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

et al. 2018

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“…In Reference [84], Poux details how a point cloud can be processed so as to extract the semantic data and store it into a point cloud database (PostgreSQL V.9.6) for semantic queries. The author shows how an SQL point query would return the name of the object that hosts the given point.…”

Section: How the Presented Sdbl Approach Relates To Previous Workmentioning

confidence: 99%

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

El-Mahgary

Virtanen

Hyyppä

2020

IJGI

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The importance of being able to separate the semantics from the actual (X,Y,Z) coordinates in a point cloud has been actively brought up in recent research. However, there is still no widely used or accepted data layout paradigm on how to efficiently store and manage such semantic point cloud data. In this paper, we present a simple data layout that makes use the semantics and that allows for quick queries. The underlying idea is especially suited for a programming approach (e.g., queries programmed via Python) but we also present an even simpler implementation of the underlying technique on a well known relational database management system (RDBMS), namely, PostgreSQL. The obtained query results suggest that the presented approach can be successfully used to handle point and range queries on large points clouds.

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“…colour information for each point) which can be used through different point cloud rendering algorithms (Mures et al, 2016). Very interestingly, point cloud can be enhanced by integrating semantics (Poux, 2019;Poux et al, 2016a) through intelligent processes such as those based on semantic segmentation (Poux and Billen, 2019a). In this article, we will investigate the use of semantics for interaction and rendering purposes for highly immersive applications and decision-making.…”

Section: Introductionmentioning

confidence: 99%

Classification and Integration of Massive 3d Points Clouds in a Virtual Reality (Vr) Environment

Kharroubi

Hajji

Billen³

et al. 2019

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

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Abstract. With the increasing volume of 3D applications using immersive technologies such as virtual, augmented and mixed reality, it is very interesting to create better ways to integrate unstructured 3D data such as point clouds as a source of data. Indeed, this can lead to an efficient workflow from 3D capture to 3D immersive environment creation without the need to derive 3D model, and lengthy optimization pipelines. In this paper, the main focus is on the direct classification and integration of massive 3D point clouds in a virtual reality (VR) environment. The emphasis is put on leveraging open-source frameworks for an easy replication of the findings. First, we develop a semi-automatic segmentation approach to provide semantic descriptors (mainly classes) to groups of points. We then build an octree data structure leveraged through out-of-core algorithms to load in real time and continuously only the points that are in the VR user's field of view. Then, we provide an open-source solution using Unity with a user interface for VR point cloud interaction and visualisation. Finally, we provide a full semantic VR data integration enhanced through developed shaders for future spatio-semantic queries. We tested our approach on several datasets of which a point cloud composed of 2.3 billion points, representing the heritage site of the castle of Jehay (Belgium). The results underline the efficiency and performance of the solution for visualizing classifieds massive point clouds in virtual environments with more than 100 frame per second.

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A Smart Point Cloud Infrastructure for intelligent environments

Cited by 22 publications

References 1 publication

3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

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

Classification and Integration of Massive 3d Points Clouds in a Virtual Reality (Vr) Environment

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