Integration of knowledge to support automatic object reconstruction from images and 3D data

Abstract

Object reconstruction is an important task in many fields of application as it allows to generate digital representations of our physical world used as base for analysis, planning, construction, visualization or other aims. A reconstruction itself normally is based on reliable data (images, 3D point clouds for example) expressing the object in his complete extent. This data then has to be compiled and analyzed in order to extract all necessary geometrical elements, which represent the object and form a digital copy of it. Traditional strategies are largely based on manual interaction and interpretation, because with increasing complexity of objects human understanding is inevitable to achieve acceptable and reliable results. But human interaction is time consuming and expensive, why many researches has already been invested to use algorithmic support, what allows to speed up the process and to reduce manual work load. Presently most of such supporting algorithms are datadriven and concentrate on specific features of the objects, being accessible to numerical models. By means of these models, which normally will represent geometrical (flatness, roughness, for example) or physical features (color, texture), the data is classified and analyzed. This is successful for objects with low complexity, but gets to its limits with increasing complexness of objects. Then purely numerical strategies are not able to sufficiently model the reality. Therefore, the intention of our approach is to take human cognitive strategy as an example, and to simulate extraction processes based on available human defined knowledge for the objects of interest. Such processes will introduce a semantic structure for the objects and guide the algorithms used to detect and recognize objects, which will yield a higher effectiveness. Hence, our research proposes an approach using knowledge to guide the algorithms in 3D point cloud and image processing.

Semantic interpretation of architectural and archaeological geometries: Point cloud segmentation for HBIM parameterisation

“…These strategies involve an automatic identification process without the need for operator intervention. Boochs et al [31] defined segmentation as the combination of algorithms that improve projective reconstruction. Grilli et al [29] defined it as the process of grouping "point clouds into multiple homogeneous regions with similar properties, while classification is the step that labels these regions".…”

“…The 3D object reconstruction is an important step in digital representation since it allows for approaching the physical world as a basis for analysis and construction [31]. Modelling has therefore been considered as a digital representation comprising simplified geometric properties of heritage buildings.…”

An Improved Automatic Pointwise Semantic Segmentation of a 3d Urban Scene From Mobile Terrestrial and Airborne Lidar Point Clouds: A Machine Learning Approach

“…(Pu, 2009) extracted semantic features using semantic rules for the reconstruction of building facades from point clouds. (Boochs, 2011), (Hmida, 2012) and (Truong, 2013) used semantic knowledge in all point cloud processing stages for object detection based on three modules including a built knowledge module, an algorithm selection module, and a semantic qualification engine. (Xing, 2018) proposed a knowledge base for feature recognition from point clouds of urban scenes.…”

Knowledge-Based Approach for 3D Reconstruction of As-Built Industrial Plant Models from Laser-Scan Data

“…The representation and use of knowledge have already been suggested as a possible solution for reconstructing objects from a 3D point cloud (Cantzler et al, 2002;Boochs et al, 2011). Recently, there is a growing consensus about such prior knowledge can assist the 3D reconstruction process of an existing large-scale facility, as this knowledge can serve as guidance in the construction industry (Tang et al, 2010).…”