bop_scene_replication

Benchmark for 6D Object Pose Estimation (BOP)
Scene Replication

This example shows how to synthetically recreate BOP scenes.

Usage

First make sure that you have downloaded a BOP dataset in the original folder structure. Also please clone the BOP toolkit.

In examples/bop_scene_replication/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

• examples/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.

Visualization

Visualize the generated data and labels:

python scripts/visHdf5Files.py example/bop_scene_replication/0.hdf5

Steps

• Loads BOP scene with object models, object poses, camera poses and camera intrinsics: loader.BopLoader module.
• Creates a point light sampled inside a shell: lighting.LightSampler module.
• Renders rgb: renderer.RgbRenderer module.
• Renders instance segmentation masks: renderer.SegMapRenderer module.
• Writes labels and images to compressed hdf5 files in output_dir: writer.Hdf5Writer module.
• Writes pose labels in BOP format to output_dir: writer.BopWriter module.

Config file

BopLoader

    {
      "module": "loader.BopLoader",
      "config": {
        "bop_dataset_path": "<args:0>/<args:1>",
        "mm2m": True,
        "split": "test", # careful, some BOP datasets only have test sets
        "scene_id": 1,
        "model_type": ""
      }
    },

If scene_id is specified, BopLoader recreates the specified scene of the BOP dataset specified by bop_dataset_path. All camera poses and intrinsics from the specified scene are also loaded.
Be careful to choose a split that is actually present in the given BOP dataset and that has ground truth.
For some BOP datasets you can choose the model_type, e.g. reconst or cad in T-LESS.

More examples

• bop_object_pose_sampling: More on sampling objects, cameras and lights.
• bop_object_physics_positioning: Drop BOP objects on planes and randomize materials