Prioritising Object Types of Industrial Facilities to Reduce As-Is Modelling Time

The cost of modelling existing industrial facilities is currently considered to counteract the benefits of the model in managing and retrofitting the facility. 90% of the modelling cost is typically spent on labour for converting point cloud data to the final model, hence reducing the cost is only possible by automating this step. Previous research has successfully validated methods for modelling specific object types such as pipes. Yet modelling is still prohibitively expensive. We tackle a part of this issue by identifying the most frequent object types that require modelling in industrial plants to guide future work aimed at automating the tedious current practice. We determine a priority list of the object types in these facilities based on their frequency of appearance (%) and intent of modelling. A parametric study based on Outer Diameter (OD) then finds the most frequent OD ranges for these objects. The results indicated that steel sections were the most frequent object type encountered in all case studies

What Objects are Most Important when Modelling Existing Industrial Facilities?

The cost of modelling existing industrial facilities currently counteracts the benefits these models provide for maintenance and retrofit of these assets. 90% of the modelling cost is spent on labor for converting point cloud data to 3D models, hence reducing the cost is only possible by automating this step. The highly-cluttered scene and large number of industrial objects increase the required modelling time. Therefore, modelling is prohibitively expensive. We tackle a part of this issue by identifying the most frequent object categories and object types that require modelling in industrial plants to guide future work aimed at automating the tedious current practice. The industrial facilities investigated are: (a) offshore platforms, (b) food processing facilities and (c) petrochemical plants. The industrial object types obtained from BIM models are hierarchically ordered based on their frequency of appearance and modelling intent. The results showed that structural elements, the piping system and electrical equipment were the most frequent object categories encountered in all case studies. The most frequent object types in these categories are then determined by implementing a statistical analysis on their frequency of appearance in all case studies. The modelling intent of the most frequent object types in these categories is then explored to determine the most important object types. These are in descending order: electrical conduit, straight pipes, circular hollow sections, elbows, channels, solid bars, I-beams, angles and flanges. Automatically modelling these frequent and critical object types can guide future researchers interested in modelling these assets

Return of the Tbx5; lineage-tracing reveals ventricular cardiomyocyte-like precursors in the injured adult mammalian heart

The single curative measure for heart failure patients is a heart transplantation, which is limited due to a shortage of donors, the need for immunosuppression and economic costs. Therefore, there is an urgent unmet need for identifying cell populations capable of cardiac regeneration that we will be able to trace and monitor. Injury to the adult mammalian cardiac muscle, often leads to a heart attack through the irreversible loss of a large number of cardiomyocytes, due to an idle regenerative capability. Recent reports in zebrafish indicate that Tbx5a is a vital transcription factor for cardiomyocyte regeneration. Preclinical data underscore the cardioprotective role of Tbx5 upon heart failure. Data from our earlier murine developmental studies have identified a prominent unipotent Tbx5-expressing embryonic cardiac precursor cell population able to form cardiomyocytes, in vivo, in vitro and ex vivo. Using a developmental approach to an adult heart injury model and by employing a lineage-tracing mouse model as well as the use of single-cell RNA-seq technology, we identify a Tbx5-expressing ventricular cardiomyocyte-like precursor population, in the injured adult mammalian heart. The transcriptional profile of that precursor cell population is closer to that of neonatal than embryonic cardiomyocyte precursors. Tbx5, a cardinal cardiac development transcription factor, lies in the center of a ventricular adult precursor cell population, which seems to be affected by neurohormonal spatiotemporal cues. The identification of a Tbx5-specific cardiomyocyte precursor-like cell population, which is capable of dedifferentiating and potentially deploying a cardiomyocyte regenerative program, provides a clear target cell population for translationally-relevant heart interventional studies

What Objects are Most Important when Modelling Existing Industrial Facilities?

The cost of modelling existing industrial facilities currently counteracts the benefits these models provide for maintenance and retrofit of these assets. 90% of the modelling cost is spent on labor for converting point cloud data to 3D models, hence reducing the cost is only possible by automating this step. The highly-cluttered scene and large number of industrial objects increase the required modelling time. Therefore, modelling is prohibitively expensive. We tackle a part of this issue by identifying the most frequent object categories and object types that require modelling in industrial plants to guide future work aimed at automating the tedious current practice. The industrial facilities investigated are: (a) offshore platforms, (b) food processing facilities and (c) petrochemical plants. The industrial object types obtained from BIM models are hierarchically ordered based on their frequency of appearance and modelling intent. The results showed that structural elements, the piping system and electrical equipment were the most frequent object categories encountered in all case studies. The most frequent object types in these categories are then determined by implementing a statistical analysis on their frequency of appearance in all case studies. The modelling intent of the most frequent object types in these categories is then explored to determine the most important object types. These are in descending order: electrical conduit, straight pipes, circular hollow sections, elbows, channels, solid bars, I-beams, angles and flanges. Automatically modelling these frequent and critical object types can guide future researchers interested in modelling these assets

State-of-practice on as-is modelling of industrial facilities

90% of the time needed for the conversion from point clouds to 3D models of industrial facilities is spent on geometric modelling due to the sheer number of Industrial Objects (IOs) of each plant. Hence, cost reduction is only possible by automating modelling. Our previous work has successfully identified the most frequent industrial objects which are in descending order: electrical conduit, straight pipes, circular hollow sections, elbows, channels, solid bars, I-beams, angles, flanges and valves. We modelled those on a state-of-the-art software, EdgeWise and then evaluated the performance of this software for pipeline and structural modelling. The modelling of pipelines is summarized in three basic steps: (a) automated extraction of cylinders, (b) their semantic classification and (c) manual extraction and editing of pipes. The results showed that cylinders are modelled with 75% recall and 62% precision on average. We discovered that pipes, electrical conduit and circular hollow sections require 80% of the Total Modelling Hours (TMH) of the 10 most frequent IOs to build the plant model. TMH was then compared to modelling hours in Revit and showed that 67% of pipe modelling time is saved by EdgeWise. This paper is the first to evaluate state-of-the-art industrial modelling software. These findings help in better understanding the problem and serve as the foundation for researchers who are interested in solving it

Prioritizing object types for modelling existing industrial facilities

The cost of modelling existing industrial facilities currently counteracts the benefits these models provide. 90% of the modelling cost is spent on converting point cloud data to 3D models due to the sheer number of Industrial Objects (IOs) of each plant. Hence, cost reduction is only possible by automating modelling. However, automatically classifying millions of IOs is a very hard classification problem due to the very large number of classes and the strong similarities between them. This paper tackles this challenge by (1) discovering the most frequent IOs and (2) measuring the man-hours required for modelling them in a state of the art software, EdgeWise. This allows to measure (a) the Total Labor Hours (TLH) spent per object type and (b) the performance of EdgeWise. We discovered that pipes, electrical conduit and circular hollow sections require 80% of the TLH needed to build the plant model. We showed that EdgeWise achieves cylinder detection with 75% recall and 62% precision. This paper is the first to discover the most laborious to model IOs and the first to evaluate state-of-the-art industrial modelling software. These findings help in better understanding the problem and serve as the foundation for researchers who are interested in solving it

CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets

Shape segmentation from point cloud data is a core step of the digital twinning process for industrial facilities. However, it is also a very labor intensive step, which counteracts the perceived value of the resulting model. The state-of-the-art method for automating cylinder detection can detect cylinders with 62% precision and 70% recall, while other shapes must then be segmented manually and shape segmentation is not achieved. This performance is promising, but it is far from drastically eliminating the manual labor cost. We argue that the use of class segmentation deep learning algorithms has the theoretical potential to perform better in terms of per point accuracy and less manual segmentation time needed. However, such algorithms could not be used so far due to the lack of a pre-trained dataset of laser scanned industrial shapes as well as the lack of appropriate geometric features in order to learn these shapes. In this paper, we tackle both problems in three steps. First, we parse the industrial point cloud through a novel class segmentation solution (CLOI-NET) that consists of an optimized PointNET++ based deep learning network and post-processing algorithms that enforce stronger contextual relationships per point. We then allow the user to choose the optimal manual annotation of a test facility by means of active learning to further improve the results. We achieve the first step by clustering points in meaningful spatial 3D windows based on their location. Then, we apply a class segmentation deep network, and output a probability distribution of all label categories per point and improve the predicted labels by enforcing post-processing rules. We finally optimize the results by finding the optimal amount of data to be used for training experiments. We validate our method on the largest richly annotated dataset of the most important to model industrial shapes (CLOI) and yield 82% average accuracy per point, 95.6% average AUC among all classes and estimated 70% labor hour savings in class segmentation. This proves that it is the first to automatically segment industrial point cloud shapes with no prior knowledge at commercially viable performance and is the foundation for efficient industrial shape modeling in cluttered point clouds

CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets

Shape segmentation from point cloud data is a core step of the digital twinning process for industrial facilities. However, it is also a very labor intensive step, which counteracts the perceived value of the resulting model. The state-of-the-art method for automating cylinder detection can detect cylinders with 62% precision and 70% recall, while other shapes must then be segmented manually and shape segmentation is not achieved. This performance is promising, but it is far from drastically eliminating the manual labor cost. We argue that the use of class segmentation deep learning algorithms has the theoretical potential to perform better in terms of per point accuracy and less manual segmentation time needed. However, such algorithms could not be used so far due to the lack of a pre-trained dataset of laser scanned industrial shapes as well as the lack of appropriate geometric features in order to learn these shapes. In this paper, we tackle both problems in three steps. First, we parse the industrial point cloud through a novel class segmentation solution (CLOI-NET) that consists of an optimized PointNET++ based deep learning network and post-processing algorithms that enforce stronger contextual relationships per point. We then allow the user to choose the optimal manual annotation of a test facility by means of active learning to further improve the results. We achieve the first step by clustering points in meaningful spatial 3D windows based on their location. Then, we apply a class segmentation deep network, and output a probability distribution of all label categories per point and improve the predicted labels by enforcing post-processing rules. We finally optimize the results by finding the optimal amount of data to be used for training experiments. We validate our method on the largest richly annotated dataset of the most important to model industrial shapes (CLOI) and yield 82% average accuracy per point, 95.6% average AUC among all classes and estimated 70% labor hour savings in class segmentation. This proves that it is the first to automatically segment industrial point cloud shapes with no prior knowledge at commercially viable performance and is the foundation for efficient industrial shape modeling in cluttered point clouds

Prioritising Object Types of Industrial Facilities to Reduce As-Is Modelling Time

The cost of modelling existing industrial facilities is currently considered to counteract the benefits of the model in managing and retrofitting the facility. 90% of the modelling cost is typically spent on labour for converting point cloud data to the final model, hence reducing the cost is only possible by automating this step. Previous research has successfully validated methods for modelling specific object types such as pipes. Yet modelling is still prohibitively expensive. We tackle a part of this issue by identifying the most frequent object types that require modelling in industrial plants to guide future work aimed at automating the tedious current practice. We determine a priority list of the object types in these facilities based on their frequency of appearance (%) and intent of modelling. A parametric study based on Outer Diameter (OD) then finds the most frequent OD ranges for these objects. The results indicated that steel sections were the most frequent object type encountered in all case studies

Prioritizing object types for modelling existing industrial facilities

The cost of modelling existing industrial facilities currently counteracts the benefits these models provide. 90 % of the modelling cost is spent on converting point cloud data to 3D models due to the sheer number of Industrial Objects (IOs) of each plant. Hence, cost reduction is only possible by automating modelling. However, automatically classifying millions of IOs is a very hard classification problem due to the very large number of classes and the strong similarities between them. This paper tackles this challenge by (1) discovering the most frequent IOs and (2) measuring the manhours required for modelling them in a state of the art software, EdgeWise. This allows to measure (a) the Total Labor Hours (TLH) spent per object type and (b) the performance of EdgeWise. We discovered that pipes, electrical conduit and circular hollow sections require 80 % of the TLH needed to build the plant model. We showed that EdgeWise achieves cylinder detection with 75% recall and 62% precision. This paper is the first to discover the most laborious to model IOs and the first to evaluate state-of-the-art industrial modelling software. These findings help in better understanding the problem and serve as the foundation for researchers who are interested in solving it