GC
G. Ceccarelli
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A point cloud is a representation of shapes, organized in a 3D irregular structure. Point clouds are increasingly used in different applications, ranging from architectural preservation to computer vision. The 3D medial axis transform is a topology preserving, skeleton representation of shapes. It can be used to decompose an object in meaningful parts and to describe local and long range information of points in a point cloud.
In the past years, many deep learning methods for point clouds emerged. These are used for different applications, such as shape classification, object detection or semantic segmentation. In particular, the latter aim to classify each point in the input point cloud in subsets, based on their semantics.
This research investigates the integration of the 3D MAT in two deep learning methods for point clouds' semantic segmentation, PointNet++ and Superpoint Graph. In particular, the 3D MAT was used in PointNet++ as a point feature, to give context to local points. Then, it was used in Superpoint Graph as a geometric descriptor to partition a point cloud and as a edge feature in the SPG.
The major findings of this research outline that the 3D MAT can be successfully used in PointNet++ as a point feature, improving the overall accuracy and loss values of the algorithm. Particularly two MAT derived properties used in this research output positive results, radii and separation angles. These can be combined with point coordinates and RGB information to bring additional knowledge on the geometry of the shape, representing its curvature and thickness. Furthermore, they can be integrated in a simple and effective way, without increasing computational or time effort in the algorithm.
The analysis carried out in Superpoint Graph depicts that the 3D MAT does not improve the initial geometric partition. In fact, adding geometric descriptors to the algorithm increases the difficulty in dividing the point cloud into simple shapes, creating artifacts. Furthermore, adding MAT information on superedges does not give added value to the SPG graph. The reason is that the SPG graph and the structured MAT are different than each other, in practice, as nodes represent diverse parts in the point cloud. ...
In the past years, many deep learning methods for point clouds emerged. These are used for different applications, such as shape classification, object detection or semantic segmentation. In particular, the latter aim to classify each point in the input point cloud in subsets, based on their semantics.
This research investigates the integration of the 3D MAT in two deep learning methods for point clouds' semantic segmentation, PointNet++ and Superpoint Graph. In particular, the 3D MAT was used in PointNet++ as a point feature, to give context to local points. Then, it was used in Superpoint Graph as a geometric descriptor to partition a point cloud and as a edge feature in the SPG.
The major findings of this research outline that the 3D MAT can be successfully used in PointNet++ as a point feature, improving the overall accuracy and loss values of the algorithm. Particularly two MAT derived properties used in this research output positive results, radii and separation angles. These can be combined with point coordinates and RGB information to bring additional knowledge on the geometry of the shape, representing its curvature and thickness. Furthermore, they can be integrated in a simple and effective way, without increasing computational or time effort in the algorithm.
The analysis carried out in Superpoint Graph depicts that the 3D MAT does not improve the initial geometric partition. In fact, adding geometric descriptors to the algorithm increases the difficulty in dividing the point cloud into simple shapes, creating artifacts. Furthermore, adding MAT information on superedges does not give added value to the SPG graph. The reason is that the SPG graph and the structured MAT are different than each other, in practice, as nodes represent diverse parts in the point cloud. ...
A point cloud is a representation of shapes, organized in a 3D irregular structure. Point clouds are increasingly used in different applications, ranging from architectural preservation to computer vision. The 3D medial axis transform is a topology preserving, skeleton representation of shapes. It can be used to decompose an object in meaningful parts and to describe local and long range information of points in a point cloud.
In the past years, many deep learning methods for point clouds emerged. These are used for different applications, such as shape classification, object detection or semantic segmentation. In particular, the latter aim to classify each point in the input point cloud in subsets, based on their semantics.
This research investigates the integration of the 3D MAT in two deep learning methods for point clouds' semantic segmentation, PointNet++ and Superpoint Graph. In particular, the 3D MAT was used in PointNet++ as a point feature, to give context to local points. Then, it was used in Superpoint Graph as a geometric descriptor to partition a point cloud and as a edge feature in the SPG.
The major findings of this research outline that the 3D MAT can be successfully used in PointNet++ as a point feature, improving the overall accuracy and loss values of the algorithm. Particularly two MAT derived properties used in this research output positive results, radii and separation angles. These can be combined with point coordinates and RGB information to bring additional knowledge on the geometry of the shape, representing its curvature and thickness. Furthermore, they can be integrated in a simple and effective way, without increasing computational or time effort in the algorithm.
The analysis carried out in Superpoint Graph depicts that the 3D MAT does not improve the initial geometric partition. In fact, adding geometric descriptors to the algorithm increases the difficulty in dividing the point cloud into simple shapes, creating artifacts. Furthermore, adding MAT information on superedges does not give added value to the SPG graph. The reason is that the SPG graph and the structured MAT are different than each other, in practice, as nodes represent diverse parts in the point cloud.
In the past years, many deep learning methods for point clouds emerged. These are used for different applications, such as shape classification, object detection or semantic segmentation. In particular, the latter aim to classify each point in the input point cloud in subsets, based on their semantics.
This research investigates the integration of the 3D MAT in two deep learning methods for point clouds' semantic segmentation, PointNet++ and Superpoint Graph. In particular, the 3D MAT was used in PointNet++ as a point feature, to give context to local points. Then, it was used in Superpoint Graph as a geometric descriptor to partition a point cloud and as a edge feature in the SPG.
The major findings of this research outline that the 3D MAT can be successfully used in PointNet++ as a point feature, improving the overall accuracy and loss values of the algorithm. Particularly two MAT derived properties used in this research output positive results, radii and separation angles. These can be combined with point coordinates and RGB information to bring additional knowledge on the geometry of the shape, representing its curvature and thickness. Furthermore, they can be integrated in a simple and effective way, without increasing computational or time effort in the algorithm.
The analysis carried out in Superpoint Graph depicts that the 3D MAT does not improve the initial geometric partition. In fact, adding geometric descriptors to the algorithm increases the difficulty in dividing the point cloud into simple shapes, creating artifacts. Furthermore, adding MAT information on superedges does not give added value to the SPG graph. The reason is that the SPG graph and the structured MAT are different than each other, in practice, as nodes represent diverse parts in the point cloud.
Student report
(2019)
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Imke Lánský, Giulia Ceccarelli, Konstantinos Mastorakis, Wessel de Jongh, Jinglan Li, Clara Garcia Sanchez, Jantien Stoter
In 2021, noise pollution monitoring will be mandatory in the Netherlands, which requires data on traffic that can be re-used for air quality estimation models. One of the important input parameters for the latter is the street type, which is required by the dilution parametrisation used within the air quality model.
The goal of this project is to show whether automatic street classification for air quality estimation is feasible and reliable, considering the geo-spatial data currently available in The Netherlands. The motivation for this project originates from the common data used in noise and air quality monitoring tools by the Dutch National Institute for Public Health and the Environment, (RIVM).
Currently, street classification is performed manually by many municipalities. The larger municipalities are legally obliged to monitor air quality levels, which makes use of the street types. Automating the process by using existing datasets can save a lot of time, costs, and resources, while providing standardised results in comparison to manual classification. In addition, our method is extendable to the whole of the Netherlands. Consequently, our method can have a large societal impact, since it allows the provision of air quality estimations for all municipalities; even those that are not yet required to do so. To our knowledge, no similar work has been conducted in this field, which made it even a bigger challenge.
The implementation of the automatic classification algorithm, which is thoroughly explained in this re- port, shows very promising results. We first tested the approaches in a small area, the Weesperstraat in Amsterdam, where we have success rates from 76.7% to 83.3% for the different classification methods when compared to the NSL classification. After evaluating the performance of each of the methods, the optimal approach has been tested on larger areas where visual inspection shows a priori promising results as well.
In addition to the automatic classification algorithm, air quality measurements with new Flow sensors from Plume Labs were performed in the city of Amsterdam. The goal was to investigate whether different street types can be identified through the use of small air quality sensors. The limited measurements did not provide distinct patterns for the different street types, and therefore identification based on pollutant concentrations was not possible within the project.
We hope that the results of this project will motivate public bodies and agencies in the Netherlands to invest in automated workflows using currently available and high accuracy geo-spatial data. This can potentially improve their efficiency, while creating a more standardised and scalable framework. ...
The goal of this project is to show whether automatic street classification for air quality estimation is feasible and reliable, considering the geo-spatial data currently available in The Netherlands. The motivation for this project originates from the common data used in noise and air quality monitoring tools by the Dutch National Institute for Public Health and the Environment, (RIVM).
Currently, street classification is performed manually by many municipalities. The larger municipalities are legally obliged to monitor air quality levels, which makes use of the street types. Automating the process by using existing datasets can save a lot of time, costs, and resources, while providing standardised results in comparison to manual classification. In addition, our method is extendable to the whole of the Netherlands. Consequently, our method can have a large societal impact, since it allows the provision of air quality estimations for all municipalities; even those that are not yet required to do so. To our knowledge, no similar work has been conducted in this field, which made it even a bigger challenge.
The implementation of the automatic classification algorithm, which is thoroughly explained in this re- port, shows very promising results. We first tested the approaches in a small area, the Weesperstraat in Amsterdam, where we have success rates from 76.7% to 83.3% for the different classification methods when compared to the NSL classification. After evaluating the performance of each of the methods, the optimal approach has been tested on larger areas where visual inspection shows a priori promising results as well.
In addition to the automatic classification algorithm, air quality measurements with new Flow sensors from Plume Labs were performed in the city of Amsterdam. The goal was to investigate whether different street types can be identified through the use of small air quality sensors. The limited measurements did not provide distinct patterns for the different street types, and therefore identification based on pollutant concentrations was not possible within the project.
We hope that the results of this project will motivate public bodies and agencies in the Netherlands to invest in automated workflows using currently available and high accuracy geo-spatial data. This can potentially improve their efficiency, while creating a more standardised and scalable framework. ...
In 2021, noise pollution monitoring will be mandatory in the Netherlands, which requires data on traffic that can be re-used for air quality estimation models. One of the important input parameters for the latter is the street type, which is required by the dilution parametrisation used within the air quality model.
The goal of this project is to show whether automatic street classification for air quality estimation is feasible and reliable, considering the geo-spatial data currently available in The Netherlands. The motivation for this project originates from the common data used in noise and air quality monitoring tools by the Dutch National Institute for Public Health and the Environment, (RIVM).
Currently, street classification is performed manually by many municipalities. The larger municipalities are legally obliged to monitor air quality levels, which makes use of the street types. Automating the process by using existing datasets can save a lot of time, costs, and resources, while providing standardised results in comparison to manual classification. In addition, our method is extendable to the whole of the Netherlands. Consequently, our method can have a large societal impact, since it allows the provision of air quality estimations for all municipalities; even those that are not yet required to do so. To our knowledge, no similar work has been conducted in this field, which made it even a bigger challenge.
The implementation of the automatic classification algorithm, which is thoroughly explained in this re- port, shows very promising results. We first tested the approaches in a small area, the Weesperstraat in Amsterdam, where we have success rates from 76.7% to 83.3% for the different classification methods when compared to the NSL classification. After evaluating the performance of each of the methods, the optimal approach has been tested on larger areas where visual inspection shows a priori promising results as well.
In addition to the automatic classification algorithm, air quality measurements with new Flow sensors from Plume Labs were performed in the city of Amsterdam. The goal was to investigate whether different street types can be identified through the use of small air quality sensors. The limited measurements did not provide distinct patterns for the different street types, and therefore identification based on pollutant concentrations was not possible within the project.
We hope that the results of this project will motivate public bodies and agencies in the Netherlands to invest in automated workflows using currently available and high accuracy geo-spatial data. This can potentially improve their efficiency, while creating a more standardised and scalable framework.
The goal of this project is to show whether automatic street classification for air quality estimation is feasible and reliable, considering the geo-spatial data currently available in The Netherlands. The motivation for this project originates from the common data used in noise and air quality monitoring tools by the Dutch National Institute for Public Health and the Environment, (RIVM).
Currently, street classification is performed manually by many municipalities. The larger municipalities are legally obliged to monitor air quality levels, which makes use of the street types. Automating the process by using existing datasets can save a lot of time, costs, and resources, while providing standardised results in comparison to manual classification. In addition, our method is extendable to the whole of the Netherlands. Consequently, our method can have a large societal impact, since it allows the provision of air quality estimations for all municipalities; even those that are not yet required to do so. To our knowledge, no similar work has been conducted in this field, which made it even a bigger challenge.
The implementation of the automatic classification algorithm, which is thoroughly explained in this re- port, shows very promising results. We first tested the approaches in a small area, the Weesperstraat in Amsterdam, where we have success rates from 76.7% to 83.3% for the different classification methods when compared to the NSL classification. After evaluating the performance of each of the methods, the optimal approach has been tested on larger areas where visual inspection shows a priori promising results as well.
In addition to the automatic classification algorithm, air quality measurements with new Flow sensors from Plume Labs were performed in the city of Amsterdam. The goal was to investigate whether different street types can be identified through the use of small air quality sensors. The limited measurements did not provide distinct patterns for the different street types, and therefore identification based on pollutant concentrations was not possible within the project.
We hope that the results of this project will motivate public bodies and agencies in the Netherlands to invest in automated workflows using currently available and high accuracy geo-spatial data. This can potentially improve their efficiency, while creating a more standardised and scalable framework.