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RandLA-Net-pytorch

This repository contains the implementation of RandLA-Net (CVPR 2020 Oral) in PyTorch.

  • We only support SemanticKITTI dataset now. (Welcome everyone to develop together and raise PR)
  • Our model is almost as good as the original implementation. (Validation set : Our 52.9% mIoU vs original 53.1%)
  • We place our pretrain-model in pretrain_model/checkpoint.tar directory.

Performance

Results on Validation Set (seq 08)

  • Compare with original implementation
Model mIoU
Original Tensorflow 0.531
Our Pytorch Implementation 0.529
  • Per class mIoU
mIoU car bicycle motorcycle truck other-vehicle person bicyclist motorcyclist road parking sidewalk other-ground building fence vegetation trunk terrain pole traffic-sign
52.9 0.919 0.122 0.290 0.660 0.444 0.515 0.676 0.000 0.912 0.421 0.759 0.001 0.878 0.354 0.844 0.595 0.741 0.517 0.414

A. Environment Setup

  1. Click this webpage and use conda to install pytorch>=1.4 (Be aware of the cuda version when installation)

  2. Install python packages

pip install -r requirements.txt
  1. Compile C++ Wrappers
bash compile_op.sh

B. Prepare Data

Download the Semantic KITTI dataset, and preprocess the data:

python data_prepare_semantickitti.py

Note:

  • Please change the dataset path in the data_prepare_semantickitti.py with your own path.
  • Data preprocessing code will convert the label to 0-19 index

C. Training & Testing

  1. Training
python3 train_SemanticKITTI.py <args>
  1. Testing
python3 test_SemanticKITTI.py <args>

Note: if the flag --index_to_label is set, output predictions will be ".label" files (label figure) which can be visualized; Otherwise, they will be ".npy" (0-19 index) files which is used to evaluated afterward.

D. Visualization & Evaluation

  1. Visualization
python3 visualize_SemanticKITTI.py <args>
  1. Evaluation
  • Example Evaluation code
python3 evaluate_SemanticKITTI.py --dataset /tmp2/tsunghan/PCL_Seg_data/sequences_0.06/ \
    --predictions runs/supervised/predictions/ --sequences 8

Acknowledgement