• Installation
  • Requirements
  • pcdet v0.5
• Getting Started
  • Requirements
  • KITTI Dataset
  • Waymo Open Dataset
  • Training & Testing

All the codes are tested in the following environment:

• Python 3.6+
• PyTorch 1.10.1
• CUDA 11.3
• wandb 0.12.11
• spconv-cu113 v2.1.21

Our implementations of 3D detectors are based on the lastest OpenPCDet. To install this pcdet library and its dependent libraries, please run the following command:

python setup.py develop

NOTE: Please re-install even if you have already installed pcdet previoursly.

Currently we provide the dataloader of KITTI dataset and Waymo dataset, and the supporting of more datasets are on the way.

• Please download the official KITTI 3D object detection dataset and organize the downloaded files as follows (the road planes could be downloaded from [road plane], which are optional for data augmentation in the training):
• If you would like to train CaDDN, download the precomputed depth maps for the KITTI training set
• NOTE: if you already have the data infos from pcdet v0.1, you can choose to use the old infos and set the DATABASE_WITH_FAKELIDAR option in tools/cfgs/dataset_configs/kitti_dataset.yaml as True. The second choice is that you can create the infos and gt database again and leave the config unchanged.

STONE-Active-3D-Detection
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2)
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2
├── pcdet
├── tools

• Generate the data infos by running the following command:

python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml

• Please download the official Waymo Open Dataset, including the training data training_0000.tar~training_0031.tar and the validation data validation_0000.tar~validation_0007.tar.
• Unzip all the above xxxx.tar files to the directory of data/waymo/raw_data as follows (You could get 798 train tfrecord and 202 val tfrecord ):

STONE-Active-3D-Detection
├── data
│   ├── waymo
│   │   │── ImageSets
│   │   │── raw_data
│   │   │   │── segment-xxxxxxxx.tfrecord
|   |   |   |── ...
|   |   |── waymo_processed_data_v0_5_0
│   │   │   │── segment-xxxxxxxx/
|   |   |   |── ...
│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1/
│   │   │── waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1.pkl
│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1_global.npy (optional)
│   │   │── waymo_processed_data_v0_5_0_infos_train.pkl (optional)
│   │   │── waymo_processed_data_v0_5_0_infos_val.pkl (optional)
├── pcdet
├── tools

• Install the official waymo-open-dataset by running the following command:

pip3 install --upgrade pip
pip3 install waymo-open-dataset-tf-2-0-0==1.2.0 --user

• Extract point cloud data from tfrecord and generate data infos by running the following command (it takes several hours, and you could refer to data/waymo/waymo_processed_data_v0_5_0 to see how many records that have been processed):

python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \
    --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml

Note that you do not need to install waymo-open-dataset if you have already processed the data before and do not need to evaluate with official Waymo Metrics.

sh scripts/${DATASET}/train_${DATASET}_backbone.sh

• STONE sampling [STONE]
• random selection [random]
• confidence sample [confidence]
• entropy sampling [entropy]
• MC-Reg sampling [montecarlo]
• greedy coreset [coreset]
• learning loss [llal]
• BADGE sampling [badge]
• CRB sampling [crb]

• Train:

python train.py --cfg_file ${CONFIG_FILE}