README.md

PointPainting (3D semantic segmentaiton)

This is naive implementation of PointPainting where any image segmentation network can be used for 3D point cloud segmentaiton where each point is labelled with a class.

Semantically Segmented Point Cloud

BEV of Point Cloud

Projection of Point Cloud on Image

Installation

pip install requirements.txt pip install timm==0.3.2 CUDA 10.1 and pytorch 1.7.1

pip install torchvision==0.8.2
pip install timm==0.3.2
pip install mmcv-full==1.2.7
pip install opencv-python==4.5.1.48
cd SegFormer && pip install -e . --user

For more details for installtion visit SegFormer

Works for any dataset (edit calibration file)

This is implemented on KITTI360

Run Instructions

python point_paint.py $PATH_TO_DATA $PATH_TO_CONFIG $PATH_TO_CHECKPOINT --device cuda:0 --palette cityscapes

python point_paint.py ./SegFormer local_configs/segformer/B5/segformer.b5.1024x1024.city.160k.py 
                ./SegFormer/segformer.b5.1024x1024.city.160k.pth --device cuda:0 --palette cityscapes

File structure

Phase1
├── SegFormer Folders
├── data    <--KITTI360
|  ├── rgb
|  ├── fused_pcd
├── calib.txt <-- calibration config from KITTI360
├── utils.py 
├── calibration.py
├── point_paint.py

References

https://github.com/AmrElsersy/PointPainting