Marius Erdt

Appointment: Adjunct Assistant Professor

Research topics:

Statistical Shape Modeling, Artificial Intelligence in Image Analysis, Computer Aided Diagnosis, Automated Segmentation and Registration of 2D and 3D data, Volume Rendering and Visualisation, Automated Image based Quality Control, Machine Learning for Visual and Medical Computing, Augmented and Virtual Reality

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

1 to 4 of 4 Results

Fully automatic CityGML-compliant city model derivation on demand(Nanyang Technological University) Aug 13, 2020 This dataverse hosts the data for the NRF funded project Fully automatic CityGML-compliant city model derivation on demand.
Source code to generate LoD1 CityGML models from city area maps Aug 12, 2020 - Fully automatic CityGML-compliant city model derivation on demand Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code to generate LoD1 CityGML models from city area maps", https://doi.org/10.21979/N9/FBWTIC, DR-NTU (Data), V2, UNF:6:zN4RTInDz93o+Dgg2Xk9oA== [fileUNF] Application source code to generate level of detail 1 models in CityGML format from an input image. In particular, the image has to be a city area map showing ground plans of buildings.
Source code and sample input CityGML models for automatic derivation of lower levels of detail Aug 11, 2020 - Fully automatic CityGML-compliant city model derivation on demand Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code and sample input CityGML models for automatic derivation of lower levels of detail", https://doi.org/10.21979/N9/9IIGWX, DR-NTU (Data), V1 This dataset contains the source code for automatically creating lower levels of detail from a given CityGML building model. E.g. a level of detail 3 model can be reduced to level of detail 0, 1, or 2. Exemplary sample models are included as well.
Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers Aug 11, 2020 - Fully automatic CityGML-compliant city model derivation on demand Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers", https://doi.org/10.21979/N9/5LJ3YC, DR-NTU (Data), V1 Source code for training a deep learning model to recognise hand written text in an image. In particular, the model recognises Singapore's city area names as well as HDB block numbers.

Fully automatic CityGML-compliant city model derivation on demand(Nanyang Technological University)

Aug 13, 2020

This dataverse hosts the data for the NRF funded project Fully automatic CityGML-compliant city model derivation on demand.

Source code to generate LoD1 CityGML models from city area maps

Aug 12, 2020 - Fully automatic CityGML-compliant city model derivation on demand

Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code to generate LoD1 CityGML models from city area maps", https://doi.org/10.21979/N9/FBWTIC, DR-NTU (Data), V2, UNF:6:zN4RTInDz93o+Dgg2Xk9oA== [fileUNF]

Application source code to generate level of detail 1 models in CityGML format from an input image. In particular, the image has to be a city area map showing ground plans of buildings.

Source code and sample input CityGML models for automatic derivation of lower levels of detail

Aug 11, 2020 - Fully automatic CityGML-compliant city model derivation on demand

Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code and sample input CityGML models for automatic derivation of lower levels of detail", https://doi.org/10.21979/N9/9IIGWX, DR-NTU (Data), V1

This dataset contains the source code for automatically creating lower levels of detail from a given CityGML building model. E.g. a level of detail 3 model can be reduced to level of detail 0, 1, or 2. Exemplary sample models are included as well.

Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers

Aug 11, 2020 - Fully automatic CityGML-compliant city model derivation on demand

Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers", https://doi.org/10.21979/N9/5LJ3YC, DR-NTU (Data), V1

Source code for training a deep learning model to recognise hand written text in an image. In particular, the model recognises Singapore's city area names as well as HDB block numbers.

Add Data

Share Dataverse

Link Dataverse

Reset Modifications