Florent Poux
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View article: Investigating Deep Learning Techniques to Estimate Fractional Vegetation Cover in the Australian Semi-arid Ecosystems combining Drone-based RGB imagery, multispectral Imagery and LiDAR data.
Investigating Deep Learning Techniques to Estimate Fractional Vegetation Cover in the Australian Semi-arid Ecosystems combining Drone-based RGB imagery, multispectral Imagery and LiDAR data. Open
Semi-arid terrestrial ecosystems exhibit sparse vegetation, characterised by herbaceous non-woody plants (e.g., forbs or grass) and woody plants (e.g., trees or shrubs). These ecosystems encounter challenges from global climate change, inc…
View article: Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds
Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds Open
Three-dimensional semantic segmentation is the foundation for automatically creating enriched Digital Twin Cities (DTCs) and their updates. For this task, prior-level fusion approaches show more promising results than other fusion levels. …
View article: Explainable Neural Subgraph Matching With Learnable Multi-Hop Attention
Explainable Neural Subgraph Matching With Learnable Multi-Hop Attention Open
Subgraph matching is a challenging problem with a wide range of applications in drug discovery, social network analysis, biochemistry, and cognitive science. It involves determining whether a given query graph is present within a larger ta…
View article: 3D Point Cloud Enriched Semantic Segmentation through Knowledge-Enhanced Deep Learning Technique
3D Point Cloud Enriched Semantic Segmentation through Knowledge-Enhanced Deep Learning Technique Open
Digital Twin Cities (DTCs) play a fundamental role in city planning and management. They allow three-dimensional modeling and simulation of cities. 3D semantic segmentation is the foundation for automatically creating enriched DTCs, as wel…
View article: 3D Indoor Mapping and BIM Reconstruction Editorial
3D Indoor Mapping and BIM Reconstruction Editorial Open
This Special Issue gathers papers reporting research on various aspects of the use of low-cost photogrammetric and lidar sensors for indoor building reconstruction. It includes contributions presenting improvements in the alignment of mobi…
View article: Three Dimensional Change Detection Using Point Clouds: A Review
Three Dimensional Change Detection Using Point Clouds: A Review Open
Change detection is an important step for the characterization of object dynamics at the earth’s surface. In multi-temporal point clouds, the main challenge is to detect true changes at different granularities in a scene subject to signifi…
View article: A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning
A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning Open
Three-dimensional digital models play a pivotal role in city planning, monitoring, and sustainable management of smart and Digital Twin Cities (DTCs). In this context, semantic segmentation of airborne 3D point clouds is crucial for modeli…
View article: 3D Point Cloud Semantic Augmentation: Instance Segmentation of 360° Panoramas by Deep Learning Techniques
3D Point Cloud Semantic Augmentation: Instance Segmentation of 360° Panoramas by Deep Learning Techniques Open
Semantic augmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionised image segmentation and classification, its impact on point cloud is an active research field. I…
View article: AN EXTENSION OF CITYJSON TO SUPPORT POINT CLOUDS
AN EXTENSION OF CITYJSON TO SUPPORT POINT CLOUDS Open
The combination between dense point clouds and 3D vector objects permits new cartographic representation of urban information. This paper proposes an extension for the CityJSON encoding to support point clouds. Following the 3.0 CityGML sp…
View article: UNSUPERVISED OBJECT-BASED CLUSTERING IN SUPPORT OF SUPERVISED POINT-BASED 3D POINT CLOUD CLASSIFICATION
UNSUPERVISED OBJECT-BASED CLUSTERING IN SUPPORT OF SUPERVISED POINT-BASED 3D POINT CLOUD CLASSIFICATION Open
The number of approaches available for semantic segmentation of point clouds has grown exponentially in recent years. The availability of numerous annotated datasets has resulted in the emergence of deep learning approaches with increasing…
View article: TESSERAE3D: A BENCHMARK FOR TESSERAE SEMANTIC SEGMENTATION IN 3D POINT CLOUDS
TESSERAE3D: A BENCHMARK FOR TESSERAE SEMANTIC SEGMENTATION IN 3D POINT CLOUDS Open
3D point cloud of mosaic tesserae is used by heritage researchers, restorers and archaeologists for digital investigations. Information extraction, pattern analysis and semantic assignment are necessary to complement the geometric informat…
View article: How to automate LiDAR point cloud sub-sampling with Python
How to automate LiDAR point cloud sub-sampling with Python Open
In this article, I will give you my two favourite 3D processes for quickly structuring and sub-sampling point cloud data with python. You will also be able to automate, export, visualize and integrate results into your favourite 3D softwar…
View article: A Built Heritage Information System Based on Point Cloud Data: HIS-PC
A Built Heritage Information System Based on Point Cloud Data: HIS-PC Open
The digital management of an archaeological site requires to store, organise, access and represent all the information that is collected on the field. Heritage building information modelling, archaeological or heritage information systems …
View article: UNSUPERVISED SEGMENTATION OF INDOOR 3D POINT CLOUD: APPLICATION TO OBJECT-BASED CLASSIFICATION
UNSUPERVISED SEGMENTATION OF INDOOR 3D POINT CLOUD: APPLICATION TO OBJECT-BASED CLASSIFICATION Open
Point cloud data of indoor scenes is primarily composed of planar-dominant elements. Automatic shape segmentation is thus valuable to avoid labour intensive labelling. This paper provides a fully unsupervised region growing segmentation ap…
View article: CityJSON Building Generation from Airborne LiDAR 3D Point Clouds
CityJSON Building Generation from Airborne LiDAR 3D Point Clouds Open
The relevant insights provided by 3D City models greatly improve Smart Cities and their management policies. In the urban built environment, buildings frequently represent the most studied and modeled features. CityJSON format proposes a l…
View article: MARKER-LESS MOBILE AUGMENTED REALITY APPLICATION FOR MASSIVE 3D POINT CLOUDS AND SEMANTICS
MARKER-LESS MOBILE AUGMENTED REALITY APPLICATION FOR MASSIVE 3D POINT CLOUDS AND SEMANTICS Open
Mobile Augmented Reality (MAR) attracts significant research and development efforts from both the industry and academia, but rarely integrate massive 3D dataset’s interactions. The emergence of dedicated AR devices and powerful Software D…
View article: AUTOMATIC 3D BUILDINGS COMPACT RECONSTRUCTION FROM LIDAR POINT CLOUDS
AUTOMATIC 3D BUILDINGS COMPACT RECONSTRUCTION FROM LIDAR POINT CLOUDS Open
Point clouds generated from aerial LiDAR and photogrammetric techniques are great ways to obtain valuable spatial insights over large scale. However, their nature hinders the direct extraction and sharing of underlying information. The gen…
View article: SELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD
SELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD Open
Automation in point cloud data processing is central for efficient knowledge discovery. In this paper, we propose an instance segmentation framework for indoor buildings datasets. The process is built on an unsupervised segmentation follow…
View article: Initial User-Centered Design of a Virtual Reality Heritage System: Applications for Digital Tourism
Initial User-Centered Design of a Virtual Reality Heritage System: Applications for Digital Tourism Open
Reality capture allows for the reconstruction, with a high accuracy, of the physical reality of cultural heritage sites. Obtained 3D models are often used for various applications such as promotional content creation, virtual tours, and im…
View article: Point Cloud vs. Mesh Features for Building Interior Classification
Point Cloud vs. Mesh Features for Building Interior Classification Open
Interpreting 3D point cloud data of the interior and exterior of buildings is essential for automated navigation, interaction and 3D reconstruction. However, the direct exploitation of the geometry is challenging due to inherent obstacles …
View article: Fundamentals to clustering high-dimensional data (3D point clouds)
Fundamentals to clustering high-dimensional data (3D point clouds) Open
Clustering algorithms allow data to be partitioned into subgroups, or clusters, in an unsupervised manner. Intuitively, these segments group similar observations together. Clustering algorithms are therefore highly dependent on how one def…
View article: 5-Step Guide to generate 3D meshes from point clouds with Python
5-Step Guide to generate 3D meshes from point clouds with Python Open
Tutorial to generate 3D meshes (.obj, .ply, .stl, .gltf) automatically from 3D point clouds using python. (Bonus) Surface reconstruction to create several Levels of Detail.
View article: Discover 3D Point Cloud Processing with Python
Discover 3D Point Cloud Processing with Python Open
Tutorial to simply set up your python environment, start processing and visualize 3D point cloud data. Highlights Anaconda, NumPy, Matplotlib and Google Colab
View article: The Future of 3D Point Clouds: a new perspective
The Future of 3D Point Clouds: a new perspective Open
Discrete spatial datasets known as point clouds often lay the groundwork for decision-making applications. But can they become the next big thing?
View article: The Smart Point Cloud Model: Integration of point intelligence
The Smart Point Cloud Model: Integration of point intelligence Open
Point cloud acquisition and processing workflows are usually application-dependent following a classic progression from data gathering to deliverable creation. While the collection step may be specific to the sensor at hands, point-cloud-a…