rhett-chen / grasp-auto-annotation

grasp labels auto-annotation

(Deprecated!!!) Grasp Label Auto-annotation

Object grasp label dense annotation based on graspnetAPI. Use the same annotation format as GraspNet1-billion dataset.

[GraspNetAPI]

Requirements

• graspnetAPI

Installation

Get the code.

git clone https://github.com/rhett-chen/grasp-auto-annotation.git
cd grasp-auto-annotation

Install packages via Pip.

pip install -r requirements.txt

Compile sdf generator(if you need to generate object sdf file)

cd sdf-gen
mkdir build && cd build && cmake ..
make

Usage

Step1

Organize your data as GraspNet1-billion format. Take OCRTOC dataset as example:
| -- ocrtoc
  | -- models
    | -- objectname1
      | -- textured.obj
      | -- textured.ply
      | -- textured.sdf
    | -- objectname2
    | -- ... ...
  | -- grasp_labels(to be generated)
    | -- objectname1_labels.npz
  | -- scenes

Step2

If object model doesn't contain .sdf file, generate .sdf from .obj use generate_sdf() or batch_generate_sdf() function in auto_annotaton/annotation_utils.py

Step3

Set relevant arguments in auto_annotaton/options.py. The default parameters here are the recommended parameters.

Step4

Generate object-level grasp pose label.

cd auto_annotation
python main.py

Step5

Load, save and visualize object grasp poses, you also need to set relevant arguments in options.py. The format of loaded grasp poses is same as GraspNetAPI(num_grasp*17).

cd auto_annotation
python load_grasp.py

Optional step

Generate pickle object dexnet model for fast loading.

cd auto_annotation
python gen_pickle_dexmodel.py

Results

We show the grasp label annotation results of large_marker object in OCRTOC dataset. When annotating this object, we set num_views=100, num_angles=3, num_depth=3, and we randomly select only one grasp point for annotaion in the example.

Acknowledgement

The process of grasping pose annotation is based on GraspNet1-billion , many function implementations are based on GraspNetAPI.