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RandLA-Net-pytorch

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

Updates:

• We extend the model with to train with one synthetic dataset, SynLiDAR
• We replaced the weighted cross entropy in the oroginal implementation with focal loss to alliverate the influence of class imbalance
• Add frequency weighted mIoU as indicator for the training & validation
• Fix some minor bugs in the original implementation
• We improve the mIoU on the validation set from 53.1% on validation set to 55.1%.
• This is a good starting point & backbone choice for those who plan to start their research on point clouds segmentation.

Previous:

• support SemanticKITTI dataset now. (Welcome everyone to develop together and raise PR)
• We place our pretrain-model in pretrain_model/checkpoint.tar directory.

Performance

Results on Validation Set (seq 08)

• Compare with original implementation

• Per class mIoU

A. Environment Setup

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

1. Install python packages

pip install -r requirements.txt

2. Compile C++ Wrappers

sh compile_op.sh

B. Prepare Data

Download the Semantic KITTI dataset, and preprocess the data:

python data_prepare_semantickitti.py --src_path path/to/sequences --dst_path destination/for/preprocessed/sequences

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>

Options:
--backbone           select the backbone to be used: choices=['randla', 'baflac', 'baaf']
--checkpoint_path    path to pretrained models(if any), otherwise train from start
--log_dir            Name of the log dir, the file will be in logs/ suffixed with start time
--max_epoch          max epoch for the model to run, default 80
--batch_size         training batch size, default 6 (indicated in oroginal implementation), modify to full utilize the GPU/s
--val_batch_size     batch size for validation, default 30
--num_workers        number of workers for I/O
--focal              whether to use focal loss or not, default True
--focal_gamma        gamma for focal loss, default 2

2. Testing

python3 test_SemanticKITTI.py <args>

Options:
--backbone           select the backbone to be used: choices=['randla', 'baflac', 'baaf']
--infer_type         all: infer all points in specified sequence, sub: subsamples in specified sequence
--checkpoint_path    required. path to the model to test
--test_id            sequence id to test
--index_to_label     whether to convert .npy(label 0-19) back .label(original labels)

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>

options:

--dataset path to dataset for visualization

--config dataset config, default utils/semantic-kitti.yaml

--sequence sequence to visualize

--predictions location for predictions

2. Evaluation

• Example Evaluation code

python3 evaluate_SemanticKITTI.py --dataset /tmp2/tsunghan/PCL_Seg_data/sequences_0.06/ \
    --predictions runs/supervised/predictions/ --sequences 8

Acknowledgement

• This repo is improved from RandLA-Net PyTorch
• Original Tensorflow implementation link