===== Panoptic Mapping Data =====
This page contains the accompanying data for the Panoptic Mapping project. The code to run the data and build panoptic maps is available at [[https://github.com/ethz-asl/panoptic_mapping]]. The paper is available on [[https://ieeexplore.ieee.org/abstract/document/9811877 | IEEE]] and [[https://arxiv.org/abs/2109.10165 | ArXiv]]:

Schmid, Lukas, et al. "Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency." in IEEE International Conference on Robotics and Automation (ICRA), pp. 8018-8024, 2022.

==== The Flat Dataset ====
The flat dataset consists of synthetic images, rendered using [[https://www.unrealengine.com/en-US/ | Unreal Engine 4]], of two trajectories in an indoor environment that was subject to change. Structural ground truth point clouds and panoptic annotations are included.


| {{ :panoptic_mapping_run1.png?200 |}} | {{ :panoptic_mapping_run2.png?200 |}}| {{ :panoptic_mapping_changes.png?200 |}}|
|   Run 1   |   Run 2   |   Changes   |

=== Data Layout ===
The following files are included (# short description). Additional processing and data-to-ROS players are available on [[https://github.com/ethz-asl/panoptic_mapping |github]].

Data:
  * run<RunNo>
    * <FrameNo>_color.png # Color image of the sequence.
    * <FrameNo>_depth.tiff # Depth image of the sequence [m].
    * <FrameNo>_segmentation.png # Ground truth panoptic labels, each pixel contains a label ID.
    * <FrameNo>_predicted.png # Detectron2 predicted panoptic labels, each pixel contains a label ID.
    * <FrameNo>_labels.json # Dictionary of accompanying information for each Detectron2 prediction.
    * <FrameNo>_pose.txt # Pose of the sensor in Odom frame.
    * timestamps.csv  # Timestamps of each frame as recorded in the simulation.
Ground Truth:
  * flat_<RunNo>_gt_10000.ply # Structural ground truth pointcloud.
Utility Files:
  * groundtruth_labels.csv # Labels of the panoptic ground truth IDs (use with the mapper).
  * groundtruth_labels_classes.csv # Names of class IDs in 'groundtruth_labels.csv'.
  * detectron_labels.csv # Labels of the panoptic detectron IDs (use with the mapper).
  * changes.txt # Explanations of the changed objects with their ground truth label names.
  * intrinsics.txt # Camera intrinsics of the images [pixels].
Log Files (You can safely ignore these)
  * airsim.yaml # Configuration of the simulator used to create the data.
  * infrared_corrections.csv # Infrared corrections when running the simulator.
  * waypoints<RunNo>.yaml # Way points traveled to when generating the data.

==== RIO demo data ====
To run the mapper on real data, two sequences of the [[ https://waldjohannau.github.io/RIO/ | RIO dataset]] [1] were evaluated. 
The original data set can not be redistributed, please download the data from [[ https://waldjohannau.github.io/RIO/ | here]]. 
We provide additional data to run Panoptic Mapping for the scenes 466 and 27, in particular, for the scan IDs:

  * 0cac7578-8d6f-2d13-8c2d-bfa7a04f8af3
  * 20c9939d-698f-29c5-85c6-3c618e00061f
  * 2451c041-fae8-24f6-9213-b8b6af8d86c1
  * ddc73793-765b-241a-9ecd-b0cebb7cf916
  * ddc73795-765b-241a-9c5d-b97744afe077
  * f62fd5f8-9a3f-2f44-8b1e-1289a3a61e26

Please download the additional data and add it to the already downloaded RIO sequences.

[1] Wald, Johanna, et al. "RIO: 3D object instance re-localization in changing indoor environments." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.

=== Additional Data Layout ===
Data:
  * <Sequence Hash>
    * sequence 
      * frame-<FrameNo>.panlables.png # Ground truth panoptic labels, each pixel contains a label ID.
      * frame-<FrameNo>.predicted.png # Detectron2 predicted panoptic labels, each pixel contains a label ID.
      * frame-<FrameNo>.detectronlabels.json # Dictionary of accompanying information for each Detectron2 prediction.
Ground Truth:
  * <Sequence Hash>
    * gt_10000.ply # Structural ground truth pointcloud for that sequence.
  * gt_scene<Scene No>.ply # Combined ground truth pointcloud for a scene.
Utility Files:
  * groundtruth_labels.csv # Labels of the panoptic ground truth IDs (use with the mapper).
  * detectron_labels.csv # Labels of the panoptic detectron IDs (use with the mapper).


==== Download ====

[[http://robotics.ethz.ch/~asl-datasets/2021_Panoptic_Mapping/flat_dataset.zip|flat_dataset (392.7 MB)]]

[[http://robotics.ethz.ch/~asl-datasets/2021_Panoptic_Mapping/rio_data.zip|rio_data (87.8 MB)]]