SensatUrban-BEV-Seg3D

This is the official implementation of our BEV-Seg3D-Net, an efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc.

Features of our framework/model:

• leveraging various proven methods in 2D segmentation for 3D tasks
• achieve competitive performance in the SensatUrban benchmark
• fast inference process, about 1km^2 area per minute with RTX 3090.

To be done:

• add more complex/efficient fusion models
• add more backbone like ResNeXt, HRNet, DenseNet, etc.
• add more novel projection methods like pointpillars

For technical details, please refer to:

Efficient Urban-scale Point Clouds Segmentation with BEV Projection
Zhenhong Zou, Yizhe Li, Xinyu Zhang

Please cite by:

@article{Zou2021EfficientUP,
  title={Efficient Urban-scale Point Clouds Segmentation with BEV Projection},
  author={Zhenhong Zou and Yizhe Li},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.09074}
}

(1) Setup

This code has been tested with Python 3.7, PyTorch 1.8, CUDA 11.0 on Ubuntu 16.04. PyTorch of earlier versions should be supported.

• Clone the repository

git clone https://github.com/zouzhenhong98/SensatUrban-BEV-Seg3D.git & cd SensatUrban-BEV-Seg3D

• Setup python environment

conda create -n bevseg python=3.7
source activate bevseg
pip install -r requirements.txt

(2) Preprocess

We provide various data analysis and preprocess methods for the SensatUrban dataset. (Part of the following steps are optional)

• Before data generation, change the path_to_your_dataset in preprocess/point_EDA_31.py by:

Sensat = SensatUrbanEDA()
Sensat.root_dir = 'path_to_your_dataset'
Sensat.split = 'train' # change to 'test' for inference

• Initialize the BEV projection arguments. We provide our optimal setting below, but you can set other values for analysis:

Sensat.grids_scale = 0.05
Sensat.grids_size = 25
Sensat.grids_step = 25

• (Optional) If you want to test the sliding window points generator:

data_dir = os.path.join(self.root_dir, self.split)
ply_list = sorted(os.listdir(data_dir))[0]
ply_path = os.path.join(data_dir, ply_name)
ply_data = self.load_points(ply_path, reformat=True)
grids_data = self.grid_generator(ply_data, self.grids_size, self.grids_step, False) # return an Iterator

• Calculating spatial overlap ratio in BEV projection:

Sensat.single_ply_analysis(Sensat.exp_point_overlay_count) # randomly select one ply file
Sensat.batch_ply_analysis(Sensat.exp_point_overlay_count) # for all ply files in the path

• Calculating class overlap ratio in BEV projection, that means we ignore overlapped points belonging to the same category:

Sensat.single_ply_analysis(Sensat.exp_class_overlay_count) # randomly select one ply file
Sensat.batch_ply_analysis(Sensat.exp_class_overlay_count) # for all ply files in the path

• Test BEV projection and 3D remapping with IoU index test (reflecting the consistency in 3D Segmentation and BEV Segmentation tasks):

Sensat.evaluate('offline', Sensat.map_offline_img2pts)

• BEV data generation:

Sensat.batch_ply_analysis(Sensat.exp_gen_bev_projection)

• Point Spatial Overlap Ratio Statistics at different projection scales

• More BEV projection testing results refers to our sample images: completion test at imgs/completion_test, edge detection with different CV operators at imgs/edge_detection, rgb and label projection samples at imgs/projection_sample

(3) Training & Inference

We provide two basic multimodal fusion network developped from U-Net in the modeling folder, unet.py is the basic feature fusion, and uneteca.py is the attention fusion.

• Change the path_to_your_dataset in mypath.py and dataloaders/init.py >>> 'cityscapes'

• Train from sratch

python train.py --use-balanced-weights --batch-size 8 --base-size 500 --crop-size 500 --loss-type focal --epochs 200 --eval-interval 1

• Change the save_dir in inference.py

• Inference on test data

python inference.py --batch-size 8

• Prediction Results Visualization (RGB, altitude, label, prediction)

(4) Evaluation

• Remap your BEV prediction to 3D and evaluate in 3D benchmark in preprocess/point_EDA_31.py (following the previous initialization steps):

Sensat.evaluate_batch(Sensat.evaluate_batch_nn(Sensat.eval_offline_img2pts))

(5) Citation

If you find our work useful in your research, please consider citing: (Information is coming soon! We are asking the open-access term of the conference!)

(6) Acknowledgment

• Part of our data processing code (read_ply and metrics) is developped based on https://github.com/QingyongHu/SensatUrban
• Our code of neural network is developped based on a U-Net repo from the github, but unfortunately we are unable to recognize the raw github repo. Please tell us if you can help.

(7) Related Work

To learn more about our fusion segmentation methods, please refers to our previous work:

@article{Zhang2021ChannelAI,
    title={Channel Attention in LiDAR-camera Fusion for Lane Line Segmentation},
    author={Xinyu Zhang and Zhiwei Li and Xin Gao and Dafeng Jin and Jun Li},
    journal={Pattern Recognit.},
    year={2021},
    volume={118},
    pages={108020}
}

@article{Zou2021ANM,
    title={A novel multimodal fusion network based on a joint coding model for lane line segmentation},
    author={Zhenhong Zou and Xinyu Zhang and Huaping Liu and Zhiwei Li and A. Hussain and Jun Li},
    journal={ArXiv},
    year={2021},
    volume={abs/2103.11114}
}