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Data Pipeline for a Single Scene

Notice: we're not actively documenting more of our pre-processing pipeline for external users, we suggest instead using our provided data which has already been pre-processed

This outlines how to collect and process data for a single scene. See here for how the dataset is organized. The steps here are split across code in two repos.

  • spartan handles the raw data collection and tsdf fusion.
  • pdc handles change detection and rendering.

Spartan

Capture Raw data with Kuka

The quick version of raw data collection currently is:

Human-moved objects
  1. Start Kuka, run Position control
  2. kip (shortcut for Kuka Iiwa Procman) then in procman:
    1. Start ROS script (check that openni driver looks happy)
    2. Run Kuka drivers
    3. Check that pointcloud / sensor data in RVIZ looks OK
  3. New terminal: prepare to collect logs via navigate to fusion server scripts:
use_ros && use_spartan
cd ~/spartan/src/catkin_projects/fusion_server/scripts
  1. Collect many raw logs, for each:
    1. Move objects to desired position
    2. ./capture_scene_client.py
    3. This will create a new folder with the current date (e.g. 2018-04-07-20-23-56) and the raw/fusion.bag file as in the folder structure above.
Autonomous robot-moved objects
  1. Start Kuka, run Position control
  2. kip (shortcut for Kuka Iiwa Procman) then in procman:
    1. Start ROS script (check that openni driver looks happy)
    2. Run Kuka drivers
    3. Check that pointcloud / sensor data in RVIZ looks OK
    4. Run Director
  3. In Director terminal (f8), enter: graspSupervisor.testInteractionLoop()

TSDF Fusion

This is done in spartan. Navigate to spartan/src/catkin_projects/fusion_server/scripts. With log_dir set to the directory of your log, i.e. the full path to 2018-04-07-20-23-56 run

./extract_and_fuse_single_scene.py <full_path_to_log_folder>

This will

  1. Extract all the rgb and depth images into processed/images
  2. Produces processed/images/camera_info.yaml which contains the camera intrinsic information.
  3. Produces processed/images/pose_data.yaml which contains the camera pose corresponding to each image.
  4. Run tsdf fusion
  5. Convert the tsdf fusion to a mesh and save it as processed/fusion_mesh.ply
  6. Downsample the images in processed/images and only keep those with poses that are sufficiently different.

PDC

Change Detection and Depth Image Rendering

This is done in pytorch-dense-correspondence. In pdc

  1. use_pytorch_dense_correspondence
  2. use_director
  3. run_change_detection --data_dir <full_path_to_log_folder>/processed
    • This will run change detection and render new depth images for the full scene and the cropped scene. The data that is produced by this step is
    • processed/rendered_images/000000_depth_cropped.png
    • processed/image_masks/000000_mask.png
    • processed/image_masks/000000_mask_visible.png
  4. render_depth_images.py --data_dir <full_path_to_log_folder>/processed
    • This will render depth images against the full tsdf reconstruction, not the cropped one.
    • Produces the file processed/rendered_images/000000_depth.png