RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor

Environment

Our code is developed and tested on the following environment:

• Python 3.6
• PyTorch 1.5.0
• Cuda 10.1
• Numpy 1.18

wandb is required to record the training procedure.

Network

The network model is defined in models/models.py.

Demo

We provides a pair of point clouds of KITTI dataset and Ford dataset in demo/pc, the pretrain model is stored in pretrain

Generates keypoints and descriptors of the sample data by run python demo.py

The keypoints and descriptors will be save in demo/results/keypoints and demo/results/desc. This step will cover the provided keypoints and descriptors.

demo/demo_reg/demo_reg.m is a matlab code to visualize registration of the sample pairs.

Training

The network should be trained in two stages,

• Firstly, train detector network using sh train_detector.sh, please change DATA_DIR to your own data.
• Secondly, train descriptor network using sh train_descriptor.sh, please change DATA_DIR to your own data and PRETRAIN_DETECTOR_MODEL to the correct path (based on the first step).