HFT: Lifting Perspective Representations via Hybrid Feature Transformation

This repositary contains the official Pytorch implementation for paper HFT: Lifting Perspective Representations via Hybrid Feature Transformation (2023 IEEE International Conference on Robotics and Automation , ICRA).

Introduction

Autonomous driving requires accurate and detailed Bird's Eye View (BEV) semantic segmentation for decision making, which is one of the most challenging tasks for high-level scene perception. Feature transformation from frontal view to BEV is the pivotal technology for BEV semantic segmentation. Existing works can be roughly classified into two categories, i.e., Camera model-Based Feature Transformation (CBFT) and Camera model-Free Feature Transformation (CFFT). In this paper, we empirically analyze the vital differences between CBFT and CFFT. The former transforms features based on the flat-world assumption, which may cause distortion of regions lying above the ground plane. The latter is limited in the segmentation performance due to the absence of geometric priors and time-consuming computation. In order to reap the benefits and avoid the drawbacks of CBFT and CFFT, we propose a novel framework with a Hybrid Feature Transformation module (HFT). Specifically, we decouple the feature maps produced by HFT for estimating the layout of outdoor scenes in BEV. Furthermore, we design a mutual learning scheme to augment hybrid transformation by applying feature mimicking. Notably, extensive experiments demonstrate that with negligible extra overhead, HFT achieves a relative improvement of 13.3% on the Argoverse dataset and 16.8% on the KITTI 3D Object datasets compared to the best-performing existing method.

Install

To use our code, please install the following dependencies:

• torch==1.9.1
• torchvison==0.10.1
• mmcv-full==1.3.15
• CUDA 9.2+

For more requirements, please see requirements.txt for details. You can refer to the guidelines to install the environment correctly.

Data Preparation

We conduct experiments of nuScenes, Argoverse, Kitti Raw, Kitti Odometry, and Kitti 3D Object. Please down the datasets and place them under /data/nuscenes/ and so on. Note that calib.json contains the intrinsics and extrinsics matrixes of every image. Please follow here to generate the BEV annotation (ann_bev_dir) for KITTI datasets. Refer to the script make_labels to get the BEV annotation for nuScenes and Argoverse, respectively. The datasets' structures look like:

Dataset Structure

data
├── nuscenes
|   ├── img_dir
|   ├── ann_bev_dir
|   ├── calib.json
├── argoversev1.0
|   ├── img_dir
|   ├── ann_bev_dir
|   ├── calib.json
├── kitti_processed
|   ├── kitti_raw
|   |   ├── img_dir
|   |   ├── ann_bev_dir
|   |   ├── calib.json
|   ├── kitti_odometry
|   |   ├── img_dir
|   |   ├── ann_bev_dir
|   |   ├── calib.json
|   ├── kitti_object
|   |   ├── img_dir
|   |   ├── ann_bev_dir
|   |   ├── calib.json

Prepare calib.json

"calib.json" contains the camera parameters of each image. Readers can generate the "calib.json" file by the instruction of nuScenes, Argoverse, Kitti Raw, Kitti Odometry, and Kitti 3D Object. We also upload calib.json for each dataset to google drive and Baidu Net Disk.

Training

Take Argoverse as an example. To train a semantic segmentation model under a specific configuration, run:

cd HFT
python -m torch.distributed.launch --nproc_per_node ${NUM_GPU} --master_port ${PORT} tools/train.py ${CONFIG} --work-dir ${WORK_DIR} --launcher pytorch

For instance, to train Argoverse under this config, run:

cd HFT
python -m torch.distributed.launch --nproc_per_node 4 --master_port 14300 tools/train.py ./configs/pyva/pyva_swin_argoverse.py --work-dir ./models_dir/pyva_swin_argoverse --launcher pytorch

Evaluation

To evaluate the performance, run the following command:

cd HFT
python -m torch.distributed.launch --nproc_per_node ${NUM_GPU} --master_port ${PORT} tools/test.py ${CONFIG} ${MODEL_PATH} --out ${SAVE_RESULT_PATH} --eval ${METRIC} --launcher pytorch

For example, we evaluate the mIoU on Argoverse under this config by running:

cd HFT
python -m torch.distributed.launch --nproc_per_node 4 --master_port 14300 tools/test.py ./configs/pyva/pyva_swin_argoverse.py ./models_dir/pyva_swin_argoverse/iter_20000.pth  --out ./results/pyva/pyva_20k.pkl --eval mIoU --launcher pytorch

Visulization

To get the visulization results of the model, we first change the output_type from 'iou' to 'seg' in the testing process. Take this config as an example.

model = dict(
    decode_head=dict(
        type='PyramidHeadArgoverse',
        num_classes=8,
        align_corners=True),
    # change the output_type from 'iou' to 'seg'
    test_cfg=dict(mode='whole',output_type='seg',positive_thred=0.5)
)

And then, we can generate the visualization results by running the following command:

python -m torch.distributed.launch --nproc_per_node 4 --master_port 14300 tools/test.py ./configs/pyva/pyva_swin_argoverse.py ./models_dir/pyva_swin_argoverse/iter_20000.pth --format-only --eval-options "imgfile_prefix=./models_dir/pyva_swin_argoverse" --launcher pytorch

Acknowledgement

Our work is partially based on mmseg. Thanks for their contributions to the research community.

Citation

If you find our work useful in your research, please cite our work:

@article{zou2022hft,
  title={HFT: Lifting Perspective Representations via Hybrid Feature Transformation},
  author={Zou, Jiayu and Xiao, Junrui and Zhu, Zheng and Huang, Junjie and Huang, Guan and Du, Dalong and Wang, Xingang},
  journal={arXiv preprint arXiv:2204.05068},
  year={2022}
}