Datasets:

FraunhoferIOSB
/

Synset-Boulevard

@@ -59,7 +59,7 @@ of optical/imaging effects on the classification ability.
 <!-- Provide the basic links for the dataset. -->
 - **Repository:** [synset.de/datasets/synset-blvd/](https://synset.de/datasets/synset-blvd/)
-- **Paper:** Sielemann, A., Wolf, S., Roschani, M., Ziehn, J. and Beyerer, J. (2024). Synset Boulevard: A Synthetic Image Dataset for VMMR. In 2024 IEEE International Conference on Robotics and Automation (ICRA).
 <!-- - **Demo [optional]:** [More Information Needed] -->
@@ -143,7 +143,7 @@ enabling an understanding of the generation / synthesis principles and allowing
 <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-- The dataset was generated in the [OCTANE](https://www.octane.org) simulation framework, particularly using path tracing through
 the [Blender Cycles](https://docs.blender.org/manual/en/latest/render/cycles/index.html) engine.
 - The 3D models of the vehicles were collected from commercial sources, mainly [Dosch Design](https://doschdesign.com/).
@@ -186,7 +186,7 @@ provided by different authors.
 <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
 The major part of annotations, including vehicle colors, segmentation masks and environmental conditions is based on ground truth data created as part of
-the scene generation / rendering process. Semantic segmentation images were rendered using the [Ogre 3D](https://www.ogre3d.org/) rendering engine plugin to OCTANE,
 which provides rasterization / shading-based image generation.
 The only manual annotation performed in the creation of the particular dataset is the mapping between 3D models and the corresponding vehicle make and

 <!-- Provide the basic links for the dataset. -->
 - **Repository:** [synset.de/datasets/synset-blvd/](https://synset.de/datasets/synset-blvd/)
+- **Paper:** Sielemann, A., Wolf, S., Roschani, M., Ziehn, J. and Beyerer, J. (2024). [Synset Boulevard: A Synthetic Image Dataset for VMMR](https://ieeexplore.ieee.org/abstract/document/10610650). In 2024 IEEE International Conference on Robotics and Automation (ICRA).
 <!-- - **Demo [optional]:** [More Information Needed] -->
 <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+- The dataset was generated in the [OCTAS](https://octas.org/) simulation framework, particularly using path tracing through
 the [Blender Cycles](https://docs.blender.org/manual/en/latest/render/cycles/index.html) engine.
 - The 3D models of the vehicles were collected from commercial sources, mainly [Dosch Design](https://doschdesign.com/).
 <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
 The major part of annotations, including vehicle colors, segmentation masks and environmental conditions is based on ground truth data created as part of
+the scene generation / rendering process. Semantic segmentation images were rendered using the [Ogre 3D](https://www.ogre3d.org/) rendering engine plugin to OCTAS,
 which provides rasterization / shading-based image generation.
 The only manual annotation performed in the creation of the particular dataset is the mapping between 3D models and the corresponding vehicle make and