Awesome

NEWS !!!

Sep. 01st, 2023: :pushpin: We upload our long-version paper of PivotNet on Arxiv. The code is being sorted out. Paper
Aug. 23rd, 2023: :rocket: :rocket: :rocket: The official implementation of our BeMapNet is released now. Enjoy it!
Jul. 14th, 2023: :clap: Our PivotNet is accepted by ICCV 2023 ! ~~The preprint paper is in progress.~~
Jun. 18th, 2023: :bulb: The Innovation-Award of the AD-Challenge goes to our MachMap Solution ! Tech-Report
May. 26th, 2023: :trophy: Our team win the Championship of the CVPR23 Online HD Map Construction Challenge ! Leaderboard
May. 16th, 2023: :pushpin: We upload our long-version paper with detailed supplementary material on Arxiv. Paper
Feb. 28th, 2023: :clap: Our BeMapNet is accepted by CVPR 2023 ! Refer to the Paper for more details.

<div align="center">Introduction</div>

Vectorized high-definition map (HD-map) construction, which focuses on the perception of centimeter-level environmental information, has attracted significant research interest in the autonomous driving community. In this paper, by delving into parameterization-based methods, we pioneer a concise and elegant scheme that adopts UNIFIED piecewise Bezier curve. In order to vectorize changeful map elements end-to-end, we elaborate a simple yet effective architecture, named Piecewise Bezier HD-map Network (BeMapNet), which is formulated as a direct set prediction paradigm and postprocessing-free. The overall architecture contains four primary components for extracting progressively richer-infos: image-level multi-scale features, semantic-level BEV feature, instance-level curve descriptors, and point-level Bezier control sequence.

<div align="center">Documentation</div>

<details open> <summary><b>Step-by-step Installation</b></summary> <\br><br>

a. Check Environment

Python >= 3.8
CUDA 11.1
# other versions of python/cuda have not been fully tested, but I think they should work as well.

b. Create a conda virtual environment and activate it. (Optional)
```
conda create -n bemapnet python=3.8 -y
conda activate bemapnet
```

c. Install PyTorch and torchvision following the official instructions.

pip3 install torch==1.10.1+cu111 torchvision==0.11.2+cu111 -f https://download.pytorch.org/whl/torch_stable.html

d. Install MMCV following the official instructions. (need GPU)
```
pip3 install -U openmim
mim install mmcv==1.7.1
```

e. Install Detectron2 following the official instructions.

python3 -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.10/index.html

f. Install BeMapNet.

git clone git@git-core.megvii-inc.com:qiaolimeng/bemapnet.git -b bemapnet-release
cd bemapnet
pip3 install -r requirement.txt

</details> <details> <summary><b>Material Preparation</b></summary> <\br><br>

a. Data: NuScenes

Download&Unzip the NuScenes dataset into your server and link it to desirable path.
```
cd /path/to/bemapnet
mkdir data
ln -s /any/path/to/your/nuscenes data/nuscenes
```

Generate Bezier-annotations from NuScenes's raw-annotations.

cd /path/to/bemapnet
python3 tools/bezier_converter/nuscenes/convert.py -d ./data -n bemapnet

OR download from here and put it into /path/to/bemapnet/data/nuscenes

cd /path/to/bemapnet
mkdir data/nuscenes/customer
cd data/nuscenes/customer
wget https://github.com/er-muyue/BeMapNet/releases/download/v1.0/bemapnet.zip .
unzip bemapnet.zip bemapnet

b. Weights: Public-Pretrain-Models

Download public pretrain-weights as backbone initialization.

cd /path/to/bemapnet
cd assets/weights
wget https://github.com/er-muyue/BeMapNet/releases/download/v1.0/efficientnet-b0-355c32eb.pth .
wget https://github.com/er-muyue/BeMapNet/releases/download/v1.0/resnet50-0676ba61.pth .
wget https://github.com/er-muyue/BeMapNet/releases/download/v1.0/upernet_swin_tiny_patch4_window7_512x512.pth .

c. Check: Project-Structure

Your project directory should be,

  assets
    | -- weights (resnet, swin-t, efficient-b0, ...)
    | -- 
  bemapnet
  configs
  data
    | -- nuscenes
      | -- samples (CAM_FRONT, CAM_FRONT_LEFT, CAM_FRONT_RIGHT, ...)
      | -- annotations
      | -- v1.0-trainval
      | -- ...
      | -- customer
        | -- bemapnet
          | -- *.npz
  tools

</details> <details> <summary><b> Training and Evluation</b></summary> <\br><br>

a. Model Training

bash run.sh train bemapnet_nuscenes_swint 30  # default: 8GPUs, bs=1, epochs=30

b. Model Evaluation

bash run.sh test bemapnet_nuscenes_swint ${checkpoint-path}

c. Reproduce with one command

bash run.sh reproduce bemapnet_nuscenes_swint

</details>

<div align="center">Models & Results</div>

<details open> <summary><b>Results on NuScenes Val Set</b></summary> <\br><br>

a. Easy-Setting --> AP-threshold is 0.5m, 1.0m, 1.5m (same as VectorMapNet / MapTR)

Model	Config	Schd	mAP<sup>divider</sup>	mAP<sup>pedcross</sup>	mAP<sup>boundary</sup>	mAP<sup>avg</sup>	Download
BeMapNet-Effb0	config	30ep	62.2	56.0	59.0	59.1	model/train/eval
BeMapNet-Res50	config	30ep	62.3	57.7	59.4	59.8	model/train/eval
BeMapNet-SwinT	config	30ep	64.4	61.2	61.7	62.4	model/train/eval
BeMapNet-Res50	config	110ep	66.7	62.6	65.1	64.8	model/train/eval
BeMapNet-SwinT	config	110ep	68.9	64.6	67.5	67.0	model/train/eval

b. Hard-Setting --> AP-threshold is 0.2m, 0.5m, 1.0m (Recommended as a more practical HD map evaluation protocol)

Model	Config	Schd	mAP<sup>divider</sup>	mAP<sup>pedcross</sup>	mAP<sup>boundary</sup>	mAP<sup>avg</sup>	Download
BeMapNet-Effb0	config	30ep	46.7	37.4	38.0	40.7	model/train/eval
BeMapNet-Res50	config	30ep	46.9	39.0	37.8	41.3	model/train/eval
BeMapNet-SwinT	config	30ep	49.1	42.2	39.9	43.7	model/train/eval
BeMapNet-Res50	config	110ep	52.7	44.5	44.2	47.1	model/train/eval
BeMapNet-SwinT	config	110ep	54.2	46.5	46.5	49.1	model/train/eval