3D_OutDet

Experiments on WADS

We found a huge amount of duplicate points in WADS dataset which distorts the nearest neighborhood hence affecting the performance of all the algorithms that uses kNN e.g., SOR, ROR, DSOR, DROR, 3D OutDet, SalsaNext etc. Hence, we need to remove the duplicate data points as the first step of data processing.

Duplicate Removal

1. Download the original WADS from https://digitalcommons.mtu.edu/wads/ (thanks for Kurup & Bos). I put the sequences in a folder named WADS
2. Find out the file named remove_duplicate.py, I put it in 3D_OutDet/dataset/ folder.
3. Open the remove_duplicate.py file and change the src_root variable pointing at your sequences folder from step 1. If you had used a different name for the folder, you need to modify the script accordingly.
4. Run remove_duplicate.py script however you like, e.g., (from PyCharm, from Terminal or from some other IDE)
5. You will find your duplicate free data in WADS2 folder in the same parent directory.

Since the KD Tree remains the same for the same point cloud during training, there is no point in calculating the KD Tree for every epoch. Rather we will save the nearest neighbors from KD tree for training. This would occupy some space in you HDD / SSD, so keep it in mind.

Pre-Compute kNN

1. Find out the file named generate_knn_dist_wads.py. I put it in 3D_OutDet/dataset/utils folder.
2. Open generate_knn_dist_wads.py and update data_dir in the argument parser pointing at the WADS2 folder created in the previous major step.
3. Run generate_knn_dist_wads.py. It will pre-compute kNN for all the sequences.

Train on WADS

1. Find and open the file train_wads.py. Change data_dir and mode_save_path accordingly.
2. Run train_wads.py
3. Wait for the training to finish.

Evaluation on WADS

1. Find and open the file eval_wads.py.
2. Change data_dir, model_save_path, test_output_path accordingly.
3. Run eval_wads.py
4. Wait for the evaluation to finish.

UPDATE

We added RandLA-Net on our WADS benchmark.

UPDATE

A CPP / TensorRT implementation is underway for deployment in real vehicle. https://github.com/sporsho/OutDetCPP