Pytorch implementation of Contact-GraspNet. This repo is based heavily on https://github.com/alinasarmiento/pytorch_contactnet. Original Tensorflow implementation can be found at: https://github.com/NVlabs/contact_graspnet
pip install cgn-pytorch
import cgn_pytorch
cgn_model, optimizer, config_dict = cgn_pytorch.from_pretrained(cpu=False)
git clone https://github.com/sebbyjp/cgn_pytorch.git
pip3 install -r requirements.txt
We're doing our visualizations in MeshCat. In a separate tab, start a meshcat server with meshcat-server
From here you can run python3 eval.py
To visualize different confidence threshold masks on the grasps, use the --threshold argument such as --threshold=0.8
To visualize different cluttered scenes (8-12 tabletop objects rendered in pyrender from ACRONYM), use the argument --scene and manually feed in a file name such as --scene=002330.npz.Your possible files are:
- 002330.npz
- 004086.npz
- 005274.npz
The model should work on any pointcloud of shape (Nx3). For most consistent results, please make sure to put the pointcloud in the world frame and center it by subtracting the mean. Do not normalize the pointcloud to a unit sphere or unit box, as "graspability" naturally changes depending on the size of the objects (so we don't want to lose that information about the scene by scaling it).