Merge branch 'iss_863_sort_examples_in_groups' of https://rmc-github.…

…robotic.dlr.de/common/BlenderProc into iss_863_sort_examples_in_groups
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diff --git a/README_BlenderProc4BOP.md b/README_BlenderProc4BOP.md
@@ -32,11 +32,11 @@ The cameras are positioned to cover the distribution of the ground-truth object
 
 ## Examples
 
-* [bop_challenge](datasets/examples/bop_challenge): Configuration files and information on how the official synthetic data for the BOP Challenge 2020 were created.
-* [bop_object_physics_positioning](datasets/examples/bop_object_physics_positioning): Drops BOP objects on a plane and randomizes materials.
-* [bop_object_on_surface_sampling](datasets/examples/bop_object_on_surface_sampling): Samples upright poses on a plane and randomizes materials.
-* [bop_scene_replication](datasets/examples/bop_scene_replication): Replicates test scenes (object poses, camera intrinsics and extrinsics) from the BOP datasets.
-* [bop_object_pose_sampling](datasets/examples/bop_object_pose_sampling): Loads BOP objects and samples the camera, light poses and object poses in a free space.
+* [bop_challenge](datasets/examples/datasets/bop_challenge): Configuration files and information on how the official synthetic data for the BOP Challenge 2020 were created.
+* [bop_object_physics_positioning](datasets/examples/datasets/bop_object_physics_positioning): Drops BOP objects on a plane and randomizes materials.
+* [bop_object_on_surface_sampling](datasets/examples/datasets/bop_object_on_surface_sampling): Samples upright poses on a plane and randomizes materials.
+* [bop_scene_replication](datasets/examples/datasets/bop_scene_replication): Replicates test scenes (object poses, camera intrinsics and extrinsics) from the BOP datasets.
+* [bop_object_pose_sampling](datasets/examples/datasets/bop_object_pose_sampling): Loads BOP objects and samples the camera, light poses and object poses in a free space.
 
 
 ## References

diff --git a/examples/README.md b/examples/README.md
@@ -69,13 +69,13 @@ We already support a lot of different datasets, check out the following examples
 * [front_3d](datasets/front_3d/README.md)
 * [front_3d_with_improved_mat](datasets/front_3d_with_improved_mat/README.md)
 * [rock_essentials](datasets/rock_essentials/README.md)
-* [haven_dataset](datasets/haven_dataset/README.md)
+* [haven](datasets/haven/README.md)
 * [ikea](datasets/ikea/README.md)
 * [pix3d](datasets/pix3d/README.md)
 * [AMASS_human_poses](datasets/amass_human_poses/README.md)
 * [suncg_basic](datasets/suncg_basic/README.md)
 * [suncg_with_cam_sampling](datasets/suncg_with_cam_sampling/README.md)
 * [suncg_with_improved_mat](datasets/suncg_with_improved_mat/README.md)
 * [suncg_with_object_replacer](datasets/suncg_with_object_replacer/README.md)
-* [replica_dataset](datasets/replica_dataset/README.md)
+* [replica](datasets/replica/README.md)
 * [rock_essentials](datasets/rock_essentials/README.md)
diff --git a/examples/advanced/dust/README.md b/examples/advanced/dust/README.md
@@ -4,29 +4,29 @@
 </p>
 
 
-The focus of this example is adding dust to a model with blender. For this example we are using the [haven dataset](../haven_dataset/README.md).
+The focus of this example is adding dust to a model with blender. For this example we are using the [haven dataset](../haven/README.md).
 
-Make sure that you have downloaded the haven dataset first, see the [haven example](../haven_dataset/README.md)
+Make sure that you have downloaded the haven dataset first, see the [haven example](../haven/README.md)
 
 ## Usage
 
 Execute in the BlenderProc main directory:
 
 ```
-python run.py examples/advanced/dust/config.yaml resources/haven/models/ArmChair_01/ArmChair_01_2k.blend resources/haven examples/haven_dataset/output
+python run.py examples/advanced/dust/config.yaml resources/haven/models/ArmChair_01/ArmChair_01_2k.blend resources/haven examples/datasets/haven/output
 ``` 
 
 * `examples/advanced/dust/config.yaml`: path to the configuration file with pipeline configuration.
 * `resources/haven/models/ArmChair_01/ArmChair_01.blend`:  Path to the blend file, from the haven dataset, browse the model folder, for all possible options
 * `resources/haven`: The folder where the `hdri` folder can be found, to load an world environment
-* `examples/haven_dataset/output`: path to the output directory.
+* `examples/datasets/haven/output`: path to the output directory.
 
 ## Visualization
 
 In the output folder you will find a series of `.hdf5` containers. These can be visualized with the script:
 
 ```
-python scripts/visHdf5Files.py examples/haven_dataset/output/*.hdf5
+python scripts/visHdf5Files.py examples/datasets/haven/output/*.hdf5
 ``` 
 
 ## Steps

diff --git a/examples/datasets/amass_human_poses/README.md b/examples/datasets/amass_human_poses/README.md
@@ -53,19 +53,19 @@ However, we will give you a step by step explanation on how to get access.
 Execute in the BlenderProc main directory:
 
 ```shell
-python run.py examples/amass_human_poses/config.yaml resources/AMASS examples/amass_human_poses/output
+python run.py examples/datasets/amass_human_poses/config.yaml resources/AMASS examples/datasets/amass_human_poses/output
 ```
 
-* `examples/amass_human_poses/config.yaml`: path to the configuration file with pipeline configuration.
+* `examples/datasets/amass_human_poses/config.yaml`: path to the configuration file with pipeline configuration.
 * `esources/AMASS`: path to the AMASS Dataset folder in resources folder
-* `examples/amass_human_poses/output`: path to the output directory
+* `examples/datasets/amass_human_poses/output`: path to the output directory
 
 ## Visualization
 
 In the output folder you will find multiple `.hdf5` files. These can be visualized with the script:
 
 ```shell
-python scripts/visHdf5Files.py examples/amass_human_poses/output/*.hdf5
+python scripts/visHdf5Files.py examples/datasets/amass_human_poses/output/*.hdf5
 ```
 
 ## Steps

diff --git a/examples/datasets/blenderkit/README.md b/examples/datasets/blenderkit/README.md
@@ -12,19 +12,19 @@ A script to download the .blend files is provided in the [scripts folder](../../
 Execute in the BlenderProc main directory:
 
 ```shell
-python run.py examples/blenderkit/config.yaml <PATH_TO_.BLEND_FILE> examples/blenderkit/output
+python run.py examples/datasets/blenderkit/config.yaml <PATH_TO_.BLEND_FILE> examples/datasets/blenderkit/output
 ``` 
 
-* `examples/blenderkit/config.yaml`: path to the configuration file with pipeline configuration.
+* `examples/datasets/blenderkit/config.yaml`: path to the configuration file with pipeline configuration.
 * `<PATH_TO_.BLEND_FILE>`: path to the downloaded .blend file, see the [scripts folder](../../scripts) for the download script. 
-* `examples/blenderkit/output`: path to the output directory.
+* `examples/datasets/blenderkit/output`: path to the output directory.
 
 ## Visualization
 
 In the output folder you will find a series of `.hdf5` containers. These can be visualized with the script:
 
 ```shell
-python scripts/visHdf5Files.py examples/blenderkit/output/*.hdf5
+python scripts/visHdf5Files.py examples/datasets/blenderkit/output/*.hdf5
 ``` 
 
 ## Steps

diff --git a/examples/datasets/bop_challenge/README.md b/examples/datasets/bop_challenge/README.md
@@ -23,20 +23,20 @@ python scripts/download_cc_textures.py
 ```
 
 ```
-python run.py examples/bop_challenge/<config_dataset.yaml> 
+python run.py examples/datasets/bop_challenge/<config_dataset.yaml> 
               <path_to_bop_data> 
               <bop_dataset_name> 
               <path_to_bop_toolkit> 
               resources/cctextures 
-              examples/bop_challenge/output
+              examples/datasets/bop_challenge/output
 ``` 
 
-* `examples/bop_challenge/<config_dataset.yaml>`: path to the pipeline configuration file.
+* `examples/datasets/bop_challenge/<config_dataset.yaml>`: path to the pipeline configuration file.
 * `<path_to_bop_data>`: path to a folder containing BOP datasets.
 * `<bop_dataset_name>`: name of BOP dataset.
 * `<path_to_bop_toolkit>`: path to a bop_toolkit folder.
 * `resources/cctextures`: path to CCTextures folder
-* `examples/bop_challenge/output`: path to an output folder where the bop_data will be saved
+* `examples/datasets/bop_challenge/output`: path to an output folder where the bop_data will be saved
 
 This creates 25 images of a single scene. To create a whole dataset, simply run the command multiple times.
 

diff --git a/examples/datasets/bop_object_on_surface_sampling/README.md b/examples/datasets/bop_object_on_surface_sampling/README.md
@@ -15,20 +15,20 @@ python scripts/download_cc_textures.py
 ```
 
 ```
-python run.py examples/bop_object_on_surface_sampling/config.yaml 
+python run.py examples/datasets/bop_object_on_surface_sampling/config.yaml 
               <path_to_bop_data> 
               <bop_dataset_name> 
               <path_to_bop_toolkit> 
               resources/cctextures 
-              examples/bop_object_on_surface_sampling/output
+              examples/datasets/bop_object_on_surface_sampling/output
 ``` 
 
-* `examples/bop_object_on_surface_sampling/config.yaml`: path to the pipeline configuration file.
+* `examples/datasets/bop_object_on_surface_sampling/config.yaml`: path to the pipeline configuration file.
 * `<path_to_bop_data>`: path to a folder containing BOP datasets.
 * `<bop_dataset_name>`: name of BOP dataset for which ground truth should be saved, e.g. lm
 * `<path_to_bop_toolkit>`: path to a bop_toolkit folder.
 * `resources/cctextures`: path to CCTextures folder
-* `examples/bop_object_on_surface_sampling/output`: path to an output folder.
+* `examples/datasets/bop_object_on_surface_sampling/output`: path to an output folder.
 
 ## Generate a dataset
 To aggregate data and labels over multiple scenes, simply run the script multiple times using the same command. As data is saved in chunks of 1000 images, you can easily distribute the data generation by running the scripts on different machines/servers and then collecting all chunks.

diff --git a/examples/datasets/bop_object_physics_positioning/README.md b/examples/datasets/bop_object_physics_positioning/README.md
@@ -15,20 +15,20 @@ python scripts/download_cc_textures.py
 ```
 
 ```
-python run.py examples/bop_object_physics_positioning/config.yaml
+python run.py examples/datasets/bop_object_physics_positioning/config.yaml
               <path_to_bop_data>
               <bop_dataset_name>
               <path_to_bop_toolkit>
               resources/cctextures 
-              examples/bop_object_physics_positioning/output
+              examples/datasets/bop_object_physics_positioning/output
 ``` 
 
-* `examples/bop_object_physics_positioning/config.yaml`: path to the config file.
+* `examples/datasets/bop_object_physics_positioning/config.yaml`: path to the config file.
 * `<path_to_bop_data>`: path to a folder containing BOP datasets
 * `<bop_dataset_name>`: name of BOP dataset for which ground truth should be saved, e.g. ycbv
 * `<path_to_bop_toolkit>`: path to the bop_toolkit folder
 * `resources/cctextures`: path to CCTextures folder
-* `examples/bop_object_physics_positioning/output`: path to an output folder
+* `examples/datasets/bop_object_physics_positioning/output`: path to an output folder
 
 ## Generate a dataset
 To aggregate data and labels over multiple scenes, simply run the script multiple times using the same command. As data is saved in chunks of 1000 images, you can easily distribute the data generation by running the scripts on different machines/servers and then collecting all chunks.

diff --git a/examples/datasets/bop_object_pose_sampling/README.md b/examples/datasets/bop_object_pose_sampling/README.md
@@ -8,24 +8,24 @@ This example shows how to load BOP objects and alternatingly sample light poses,
 
 First make sure that you have downloaded a [BOP dataset](https://bop.felk.cvut.cz/datasets/) in the original folder structure. Also please clone the [BOP toolkit](https://github.com/thodan/bop_toolkit).
 
-In [examples/bop_object_pose_sampling/config.yaml](config.yaml) set the `blender_install_path` where Blender is or should be installed.
+In [examples/datasets/bop_object_pose_sampling/config.yaml](config.yaml) set the `blender_install_path` where Blender is or should be installed.
 
 Execute in the BlenderProc main directory:  
 
 ```
-python run.py examples/bop_object_pose_sampling/config.yaml <path_to_bop_data> <bop_dataset_name> <path_to_bop_toolkit> examples/bop_object_pose_sampling/output
+python run.py examples/datasets/bop_object_pose_sampling/config.yaml <path_to_bop_data> <bop_dataset_name> <path_to_bop_toolkit> examples/datasets/bop_object_pose_sampling/output
 ```
-* `examples/bop_object_pose_sampling/config.yaml`: path to the pipeline configuration file.
+* `examples/datasets/bop_object_pose_sampling/config.yaml`: path to the pipeline configuration file.
 * `<path_to_bop_data>`: path to a folder containing BOP datasets.
 * `<bop_dataset_name>`: name of BOP dataset, e.g. lm
 * `<path_to_bop_toolkit> `: path to the BOP toolkit containing dataset parameters, etc.
-* `examples/bop_object_pose_sampling/output`: path to the output directory.
+* `examples/datasets/bop_object_pose_sampling/output`: path to the output directory.
 
 ## Visualization
 
 Visualize the generated data and labels:
 ```
-python scripts/visHdf5Files.py examples/bop_object_pose_sampling/output/0.hdf5
+python scripts/visHdf5Files.py examples/datasets/bop_object_pose_sampling/output/0.hdf5
 ```
 
 Alternatively, since we generated COCO annotations, you can also visualize the generated coco_annotations.json file:

diff --git a/examples/datasets/bop_scene_replication/README.md b/examples/datasets/bop_scene_replication/README.md
@@ -8,18 +8,18 @@ This example shows how to synthetically recreate BOP scenes.
 
 First make sure that you have downloaded a [BOP dataset](https://bop.felk.cvut.cz/datasets/) in the original folder structure. Also please clone the [BOP toolkit](https://github.com/thodan/bop_toolkit).
 
-In [examples/bop_scene_replication/config.yaml](config.yaml) set the `blender_install_path` where Blender is or should be installed.
+In [examples/datasets/bop_scene_replication/config.yaml](config.yaml) set the `blender_install_path` where Blender is or should be installed.
 
 Execute in the BlenderProc main directory: 
 
 ```
-python run.py examples/bop_scene_replication/config.yaml <path_to_bop_data> <bop_dataset_name> <path_to_bop_toolkit> examples/bop_scene_replication/output
+python run.py examples/datasets/bop_scene_replication/config.yaml <path_to_bop_data> <bop_dataset_name> <path_to_bop_toolkit> examples/datasets/bop_scene_replication/output
 ```
-* `examples/bop_scene_replication/config.yaml`: path to the pipeline configuration file.
+* `examples/datasets/bop_scene_replication/config.yaml`: path to the pipeline configuration file.
 * `<path_to_bop_data>`: path to a folder containing BOP datasets.
 * `<bop_dataset_name>`: name of BOP dataset, e.g. tless
 * `<path_to_bop_toolkit> `: path to the BOP toolkit containing dataset parameters, etc.
-* `examples/bop_scene_replication/output`: path to the output directory.
+* `examples/datasets/bop_scene_replication/output`: path to the output directory.
 
 ## Visualization
 

diff --git a/examples/datasets/front_3d/README.md b/examples/datasets/front_3d/README.md
@@ -38,23 +38,23 @@ Best regards,
 Execute in the BlenderProc main directory:
 
 ```
-python run.py examples/front_3d/config.yaml {PATH_TO_3D-Front-Json-File} {PATH_TO_3D-Future} {PATH_TO_3D-Front-texture} examples/front_3d/output 
+python run.py examples/datasets/front_3d/config.yaml {PATH_TO_3D-Front-Json-File} {PATH_TO_3D-Future} {PATH_TO_3D-Front-texture} examples/datasets/front_3d/output 
 ```
 
-* `examples/front_3d/config.yaml`: path to the configuration file with pipeline configuration.
+* `examples/datasets/front_3d/config.yaml`: path to the configuration file with pipeline configuration.
 
 The three arguments afterwards are used to fill placeholders like `<args:0>` inside this config file.
 * `PATH_TO_3D-Front-Json-File`: path to the 3D-Front json file 
 * `PATH_TO_3D-Future`: path to the folder where all 3D-Future objects are stored 
 * `PATH_TO_3D-Front-texture`: path to the folder where all 3D-Front textures are stored 
-* `examples/front_3d/output`: path to the output directory
+* `examples/datasets/front_3d/output`: path to the output directory
 
 ## Visualization
 
 Visualize the generated data:
 
 ```
-python scripts/visHdf5Files.py examples/front_3d/output/0.hdf5
+python scripts/visHdf5Files.py examples/datasets/front_3d/output/0.hdf5
 ```
 
 ## Steps

diff --git a/examples/datasets/front_3d_with_improved_mat/README.md b/examples/datasets/front_3d_with_improved_mat/README.md
@@ -14,17 +14,17 @@ It is also necessary to download the textures from cc_textures, we provide a scr
 Execute in the BlenderProc main directory:
 
 ```
-python run.py examples/front_3d_with_improved_mat/config.yaml {PATH_TO_3D-Front-Json-File} {PATH_TO_3D-Future} {PATH_TO_3D-Front-texture} resources/cctextures examples/front_3d_with_improved_mat/output  
+python run.py examples/datasets/front_3d_with_improved_mat/config.yaml {PATH_TO_3D-Front-Json-File} {PATH_TO_3D-Future} {PATH_TO_3D-Front-texture} resources/cctextures examples/datasets/front_3d_with_improved_mat/output  
 ```
 
-* `examples/front_3d_with_improved_mat/config.yaml`: path to the configuration file with pipeline configuration.
+* `examples/datasets/front_3d_with_improved_mat/config.yaml`: path to the configuration file with pipeline configuration.
 
 The three arguments afterwards are used to fill placeholders like `<args:0>` inside this config file.
 * `PATH_TO_3D-Front-Json-File`: path to the 3D-Front json file 
 * `PATH_TO_3D-Future`: path to the folder where all 3D-Future objects are stored 
 * `PATH_TO_3D-Front-texture`: path to the folder where all 3D-Front textures are stored 
 * `resources/cctextures`: path to the cc texture folder
-* `examples/front_3d_with_improved_mat/output`: path to the output directory
+* `examples/datasets/front_3d_with_improved_mat/output`: path to the output directory
 
 Be aware that the default path for the CCMaterialLoader is used, if you want to change this please refer to the documentation in the `CCMaterialLoader` class.
 
@@ -33,7 +33,7 @@ Be aware that the default path for the CCMaterialLoader is used, if you want to
 Visualize the generated data:
 
 ```
-python scripts/visHdf5Files.py examples/front_3d_with_improved_mat/output/0.hdf5
+python scripts/visHdf5Files.py examples/datasets/front_3d_with_improved_mat/output/0.hdf5
 ```
 
 ## Steps

diff --git a/examples/datasets/haven_dataset/README.md → examples/datasets/haven/README.md b/examples/datasets/haven_dataset/README.md → examples/datasets/haven/README.md
@@ -18,20 +18,20 @@ This will download all 3D models, all environment HDRs and also all textures the
 Execute in the BlenderProc main directory:
 
 ```
-python run.py examples/haven_dataset/config.yaml resources/haven/models/ArmChair_01/ArmChair_01_2k.blend resources/haven examples/haven_dataset/output
+python run.py examples/datasets/haven/config.yaml resources/haven/models/ArmChair_01/ArmChair_01_2k.blend resources/haven examples/datasets/haven/output
 ``` 
 
-* `examples/haven_dataset/config.yaml`: path to the configuration file with pipeline configuration.
+* `examples/datasets/haven/config.yaml`: path to the configuration file with pipeline configuration.
 * `resources/haven/models/ArmChair_01/ArmChair_01.blend`:  Path to the blend file, from the haven dataset, browse the model folder, for all possible options
 * `resources/haven`: The folder where the `hdri` folder can be found, to load an world environment
-* `examples/haven_dataset/output`: path to the output directory.
+* `examples/datasets/haven/output`: path to the output directory.
 
 ## Visualization
 
 In the output folder you will find a series of `.hdf5` containers. These can be visualized with the script:
 
 ```
-python scripts/visHdf5Files.py examples/haven_dataset/output/*.hdf5
+python scripts/visHdf5Files.py examples/datasets/haven/output/*.hdf5
 ``` 
 
 ## Steps

diff --git a/examples/datasets/haven_dataset/config.yaml → examples/datasets/haven/config.yaml b/examples/datasets/haven_dataset/config.yaml → examples/datasets/haven/config.yaml
diff --git a/...tasets/haven_dataset/rendered_example.jpg → examples/datasets/haven/rendered_example.jpg b/...tasets/haven_dataset/rendered_example.jpg → examples/datasets/haven/rendered_example.jpg
diff --git a/examples/datasets/ikea/README.md b/examples/datasets/ikea/README.md
@@ -23,19 +23,19 @@ A script to download the .obj files is provided in the [scripts folder](../../sc
 Execute in the BlenderProc main directory:
 
 ```shell
-python run.py examples/ikea/config.yaml <PATH_TO_IKEA> examples/ikea/output
+python run.py examples/datasets/ikea/config.yaml <PATH_TO_IKEA> examples/datasets/ikea/output
 ``` 
 
-* `examples/ikea/config.yaml`: path to the configuration file with pipeline configuration.
+* `examples/datasets/ikea/config.yaml`: path to the configuration file with pipeline configuration.
 * `<PATH_TO_IKEA>`: path to the downloaded IKEA dataset, see the [scripts folder](../../scripts) for the download script. 
-* `examples/ikea/output`: path to the output directory.
+* `examples/datasets/ikea/output`: path to the output directory.
 
 ## Visualization
 
 In the output folder you will find a series of `.hdf5` containers. These can be visualized with the script:
 
 ```shell
-python scripts/visHdf5Files.py examples/ikea/output/*.hdf5
+python scripts/visHdf5Files.py examples/datasets/ikea/output/*.hdf5
 ``` 
 
 ## Steps