Reconstruction and Analysis of 3D Scenes

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

Billen

2017

ABSTRACT:Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor's biased data, each tessera in the highdensity point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour's class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud.

Section: Study Sitementioning

confidence: 99%

Point Cloud Classification of Tesserae From Terrestrial Laser Data Combined With Dense Image Matching for Archaeological Information Extraction

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

Billen

2017

“…Nested Octrees are used for rendering in order to accommodate the point density and repartition. However, class attributes and features are stored in a different structure while retaining a direct link to the correct point/patch index, as discuss in (Weinmann, 2016).…”

Section: Point Cloud Modelmentioning

confidence: 99%

Digital Investigations of an Archaeological Smart Point Cloud: A Real Time Web-Based Platform to Manage the Visualisation of Semantical Queries

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

Hallot

et al. 2017

ABSTRACT:While virtual copies of the real world tend to be created faster than ever through point clouds and derivatives, their working proficiency by all professionals' demands adapted tools to facilitate knowledge dissemination. Digital investigations are changing the way cultural heritage researchers, archaeologists, and curators work and collaborate to progressively aggregate expertise through one common platform. In this paper, we present a web application in a WebGL framework accessible on any HTML5-compatible browser. It allows real time point cloud exploration of the mosaics in the Oratory of Germigny-des-Prés, and emphasises the ease of use as well as performances. Our reasoning engine is constructed over a semantically rich point cloud data structure, where metadata has been injected a priori. We developed a tool that directly allows semantic extraction and visualisation of pertinent information for the end users. It leads to efficient communication between actors by proposing optimal 3D viewpoints as a basis on which interactions can grow.

“…This shows a need to prioritize and find robust and relevant features to address the heterogeneity in a point cloud structure. We propose to build on (Weinmann, 2016) classification of 3D descriptors in three categories being point attributes (sensor descriptors obtain through measurements), shape and local features. We can extend the definition by adding structure descriptors which include global descriptors and structure generalization through abstraction-features for segmentation.…”

Section: Analytical and Geometry Featuringmentioning

confidence: 99%

“…KD-Tree or derived octrees such as 3DOR Tree are performing well for such intensive tasks, therefore this is an analytical-structure prior to final classification that will be further investigated for storing points. However, attributes and features are to be stored in a different structure while retaining a direct link to the correct point/patch index, as discuss in (Weinmann, 2016). The structure should avoid any costly rebuilding operation when deleting or inserting points for example.…”

Section: Proposed Frameworkmentioning

confidence: 99%

Smart Point Cloud: Definition and Remaining Challenges

Hallot

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

et al. 2016

Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper identifies remaining challenges for a new data structure: the <i>smart point cloud</i>. This concept arises with the statement that massive and discretized spatial information from active remote sensing technology is often underused due to data mining limitations. The generalisation of point cloud data associated with the heterogeneity and temporality of such datasets is the main issue regarding structure, segmentation, classification, and interaction for an immediate understanding. We propose to use both point cloud properties and human knowledge through machine learning to rapidly extract pertinent information, using user-centered information (smart data) rather than raw data. A review of feature detection, machine learning frameworks and database systems indexed both for mining queries and data visualisation is studied. Based on existing approaches, we propose a new 3-block flexible framework around device expertise, analytic expertise and domain base reflexion. This contribution serves as the first step for the realisation of a comprehensive smart point cloud data structure.