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This work is the official Pytorch implementation of our ICCV publication: KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection.

[ICCV Page] [arXiv] [Supp]

News

:fire: 05/28 updates: a bug in SECOND-IoU fixed; will upload all vis data today.

:fire: 08/13 updates: under development. Checkpoints will be uploaded soon.

:fire: 03/18 updates: The checkpoints can be found via https://drive.google.com/drive/folders/1xbEI3tSfTCHIt3m8tk4hAy4qBJ55NuqL?usp=sharing

Citation

@inproceedings{DBLP:conf/iccv/LuoCF0BH23,
  author       = {Yadan Luo and
                  Zhuoxiao Chen and
                  Zhen Fang and
                  Zheng Zhang and
                  Mahsa Baktashmotlagh and
                  Zi Huang},
  title        = {Kecor: Kernel Coding Rate Maximization for Active 3D Object Detection},
  booktitle    = {{IEEE/CVF} International Conference on Computer Vision, {ICCV} 2023, Paris, France, October 1-6, 2023},
  pages        = {18233--18244},
  publisher    = {{IEEE}},
  year         = {2023}
}

Framework

Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but its success hinges on obtaining large amounts of precise 3D annotations. Active learning (AL) seeks to mitigate the annotation burden through algorithms that use fewer labels and can attain performance comparable to fully supervised learning. Although AL has shown promise, current approaches prioritize the selection of unlabeled point clouds with high aleatoric and/or epistemic uncertainty, leading to the selection of more instances for labeling and reduced computational efficiency. In this paper, we resort to a novel kernel coding rate maximization (KECOR) strategy which aims to identify the most informative point clouds to acquire labels through the lens of information theory. Greedy search is applied to seek desired point clouds that can maximize the minimal number of bits required to encode the latent features. To determine the uniqueness and informativeness of the selected samples from the model perspective, we construct a proxy network of the 3D detector head and compute the outer product of Jacobians from all proxy layers to form the empirical neural tangent kernel (NTK) matrix. To accommodate both one-stage (i.e., SECOND) and two-stage detectors (i.e., PV-RCNN), we further incorporate the classification entropy maximization and well trade-off between detection performance and the total number of bounding boxes selected for annotation. Extensive experiments conducted on two 3D benchmarks and a 2D detection dataset evidence the superiority and versatility of the proposed approach. Our results show that approximately 44% box-level annotation costs and 26% computational time are reduced compared to the state-of-the-art AL method, without compromising detection performance.

Contents

Installation

Requirements

All the codes are tested in the following environment:

Install pcdet v0.5

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.

Getting Started

The active learning configs are located at tools/cfgs/active-kitti_models and /tools/cfgs/active-waymo_models for different AL methods. The dataset configs are located within tools/cfgs/dataset_configs, and the model configs are located within tools/cfgs for different datasets.

Dataset Preparation

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

KITTI Dataset

KECOR-active-3Ddet
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2)
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2
├── pcdet
├── tools

python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml
<!-- ### NuScenes Dataset * Please download the official [NuScenes 3D object detection dataset](https://www.nuscenes.org/download) and organize the downloaded files as follows: ``` KECOR-active-3Ddet ├── data │ ├── nuscenes │ │ │── v1.0-trainval (or v1.0-mini if you use mini) │ │ │ │── samples │ │ │ │── sweeps │ │ │ │── maps │ │ │ │── v1.0-trainval ├── pcdet ├── tools ``` * Install the `nuscenes-devkit` with version `1.0.5` by running the following command: ```shell script pip install nuscenes-devkit==1.0.5 ``` * Generate the data infos by running the following command (it may take several hours): ```python python -m pcdet.datasets.nuscenes.nuscenes_dataset --func create_nuscenes_infos \ --cfg_file tools/cfgs/dataset_configs/nuscenes_dataset.yaml \ --version v1.0-trainval ``` -->

Waymo Open Dataset

KECOR-active-3Ddet
├── 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

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

Waymo version in our project is 1.2.0

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.

Training & Testing

Test and evaluate the pretrained models

The weights of our pre-trained model will be released upon acceptance.

python test.py --cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE} --ckpt ${CKPT}

python test.py --cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE} --eval_all

sh scripts/dist_test.sh ${NUM_GPUS} \
    --cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE}

# or

sh scripts/slurm_test_mgpu.sh ${PARTITION} ${NUM_GPUS} \
    --cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE}

Train a backbone

In our active learning setting, the 3D detector will be pre-trained with a small labeled set $\mathcal{D}_L$ which is randomly sampled from the trainig set. To train such a backbone, please run

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

Train with different active learning strategies

We provide several options for active learning algorithms, including

To add your own AL algorithm, please simply add a function under pcdet/query_strategies

You could optionally add extra command line parameters --batch_size ${BATCH_SIZE} and --epochs ${EPOCHS} to specify your preferred parameters.

python train.py --cfg_file ${CONFIG_FILE}

Acknowledgement

Part of code for NTK implementation is from https://github.com/dholzmueller/bmdal_reg