Self-supervised point set local descriptors for point cloud registration. (SS-FeatNet)

Yuan, Y., Borrmann, D., Hou, J., Ma, Y., Nüchter, A., & Schwertfeger, S. (2021). Self-supervised point set local descriptors for point cloud registration. Sensors, 21(2), 486.

Environment

Our code is developed on top of the implementation of 3DFeatNet.

Please follow 3DFeatNet to preprocess the data.

Repo structure

./use_descriptor
-> train.sh # bash script to train model
-> exp/ # bash scripts to directly run experiments
-> models/ # network
-> tf_ops/ # PN clustering operation 
-> cpp/ # preprocess including ISS-keypoints and FPFH-keypoints (not used)
-> data/ # data augmentation and loading
-> ckpt/ # model checkpoints
-> inference.py
-> train1jit.py
-> config.py