SANE Shape-completion
Reading:
A starting point for a good 3D point-cloud annotation software is here:
Environment
Tested on Debian 9.9, Cuda: 10.0, Python: 3.6, Pytorch: 1.2.0 with Anaconda
Installation
git clone --recursive https://github.com/ziliHarvey/smart-annotation-pointrcnn.git
cd app/PointCNN/
sh build_and_install.sh
If you are using anaconda, install the environment dependencies by using environment.yaml
Testing file is at app/rl_gan/test_rl_module.py
Usage
Works on app/test_dataset/0_drive_0064_sync/sample/argoverse/lidar
cd app
python app.py
And open your browser and go to http://0.0.0.0:7772.
Testing
- Draw an approximate bounding box in one-click by pressing "a" and clicking anywhere near object
- Tick over option "Point Completion" on the left button panel to get extra points to complete the pointcloud
- Tick over option "Shape Completion" to complete the points and get a shape of object using Convex-hulling
Debugging
- Use pdb
- Use visdom provided to display output plots of completion
Progress
- Adding PointCNN Segmentation model
- Adding PointRCNN Box regresssion backend
- One-click box fitting
- Segmented object points display
- Incorporate RL-GAN-Net to do point-completion (Inference) (Training using Car Shapenet-models)
- Upgrade Encode decoder to PointCompletion Network to get robust Point Completion
- Display Shape completion with Convex hulling
- Adding Kalman filter tracking
- Use Deep learning model to do direct shape completion with image & Pointcloud