Part-A2-Free

Model description

In this work, we propose the part-aware and aggregation neural network (PartA2-Net). The whole framework consists of the part-aware stage and the part-aggregation stage. Firstly, the part-aware stage for the first time fully utilizes free-of-charge part supervisions derived from 3D ground-truth boxes to simultaneously predict high quality 3D proposals and accurate intra-object part locations. The predicted intra-object part locations within the same proposal are grouped by our new-designed RoI-aware point cloud pooling module, which results in an effective representation to encode the geometry-specific features of each 3D proposal. Then the part-aggregation stage learns to re-score the box and refine the box location by exploring the spatial relationship of the pooled intra-object part locations. At the time of submission (July-9 2019), our PartA2-Net outperforms all existing 3D detection methods and achieves new state-of-the-art on KITTI 3D object detection learderbaord by utilizing only the LiDAR point cloud data.

Step 1: Installation

## install libGL and libboost
yum install mesa-libGL
yum install boost-devel

# Install spconv
cd toolbox/spconv
bash clean_spconv.sh
bash build_spconv.sh
bash install_spconv.sh

# Install openpcdet
cd toolbox/openpcdet
pip3 install -r requirements.txt
bash build_openpcdet.sh
bash install_openpcdet.sh

Step 2: Preparing datasets

Download the kitti dataset from http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d

Download the "planes" subdataset from https://drive.google.com/file/d/1d5mq0RXRnvHPVeKx6Q612z0YRO1t2wAp/view?usp=sharing

OpenPCDet
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2)
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2
├── pcdet
├── tools

# Modify the `DATA_PATH` in the kitti_dataset.yaml to your own
cd toolbox/openpcdet
python3 -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml

Step 3: Training

Single GPU training

cd tools
python3 train.py --cfg_file cfgs/kitti_models/PartA2_free.yaml

Multiple GPU training

bash scripts/dist_train.sh 16 --cfg_file cfgs/kitti_models/PartA2_free.yaml