MagicDrive

This repository contains the implementation of the paper

MagicDrive: Street View Generation with Diverse 3D Geometry Control
Ruiyuan Gao^1*, Kai Chen^2*, Enze Xie^{3^}, Lanqing Hong³, Zhenguo Li³, Dit-Yan Yeung², Qiang Xu^{1^}
¹CUHK ²HKUST ³Huawei Noah's Ark Lab
^*Equal Contribution ^{^}Corresponding Authors

Recent advancements in diffusion models have significantly enhanced the data synthesis with 2D control. Yet, precise 3D control in street view generation, crucial for 3D perception tasks, remains elusive. Specifically, utilizing Bird’s-Eye View (BEV) as the primary condition often leads to challenges in geometry control (e.g., height), affecting the representation of object shapes, occlusion patterns, and road surface elevations, all of which are essential to perception data synthesis, especially for 3D object detection tasks. In this paper, we introduce MAGICDRIVE, a novel street view generation framework offering diverse 3D geometry controls, including camera poses, road maps, and 3D bounding boxes, together with textual descriptions, achieved through tailored encoding strategies. Besides, our design incorporates a cross-view attention module, ensuring consistency across multiple camera views. With MAGICDRIVE, we achieve high-fidelity street-view synthesis that captures nuanced 3D geometry and various scene descriptions, enhancing tasks like BEV segmentation and 3D object detection.

Method

In MagicDrive, we employ two strategies (cross-attention and additive encoder branch) to inject text prompt, camera pose, object boxes, and road maps as conditions for generation. We also propose cross-view attention module for multiview consistency.

TODO

config and pretrained weight for base resolution (224x400)
demo for base resolution (224x400)
GUI for interactive bbox editing
train and test code release
config and pretrained weight for high resolution
train and test code for CVT and BEVFusion

Getting Started

Environment Setup

Clone this repo with submodules

git clone --recursive https://github.com/cure-lab/MagicDrive.git

The code is tested with Pytorch==1.10.2 and cuda 10.2 on V100 servers. To setup the python environment, follow:

cd ${ROOT}
pip install -r requirements/dev.txt
# continue to install `third_party`s

# otherwise, to run GUI only
pip install -r requirements/gui.txt
# our GUI does not need mm-series packages.
# please also install diffusers from `third_party`.

We opt to install the source code for the following packages, with cd ${FOLDER}; pip -vvv install .

# install third-party
third_party/
├── bevfusion -> based on db75150
├── diffusers -> based on v0.17.1 (afcca39)
└── xformers  -> based on v0.0.19 (8bf59c9), optional

see note about our xformers. If you have issues with environment setup, please check FAQ first.

Setup default configuration for accelearte with

accelerate config

Our default log directory is ${ROOT}/magicdrive-log. Please be prepared.

Pretrained Weights

Our training are based on stable-diffusion-v1-5. We assume you put them at ${ROOT}/pretrained/ as follows:

{ROOT}/pretrained/stable-diffusion-v1-5/
├── text_encoder
├── tokenizer
├── unet
├── vae
└── ...

Street-view Generation with MagicDrive

Download our pretrained weight for MagicDrive from onedrive and put it in ${ROOT}/pretrained/

Run our demo

We recommand users to run our interactive GUI first, because we have minimize the dependencies for GUI demo.

cd ${ROOT}
python demo/interactive_gui.py
# a gradio-based gui, use your web browser

Run our demo for camera view generation.

cd ${ROOT}
python demo/run.py resume_from_checkpoint=magicdrive-log/SDv1.5mv-rawbox_2023-09-07_18-39_224x400

The generated images will be located in magicdrive-log/test. More information can be find in demo doc.

Train MagicDrive

Prepare Data

We prepare the nuScenes dataset similar to bevfusion's instructions. Specifically,

Download the nuScenes dataset from the website and put them in ./data/. You should have these files:

data/nuscenes
├── maps
├── mini
├── samples
├── sweeps
├── v1.0-mini
└── v1.0-trainval

Generate mmdet3d annotation files by:

python tools/create_data.py nuscenes --root-path ./data/nuscenes \
  --out-dir ./data/nuscenes_mmdet3d_2 --extra-tag nuscenes

You should have these files:

data/nuscenes_mmdet3d_2
├── nuscenes_dbinfos_train.pkl (-> ${bevfusion-version}/nuscenes_dbinfos_train.pkl)
├── nuscenes_gt_database (-> ${bevfusion-version}/nuscenes_gt_database)
├── nuscenes_infos_train.pkl
└── nuscenes_infos_val.pkl

Note: As shown above, some files can be soft-linked with the original version from bevfusion. If some of the files is located in data/nuscenes, you can move them to data/nuscenes_mmdet3d_2 manually.

(Optional) To accelerate data loading, we prepared cache files in h5 format for BEV maps. They can be generated through tools/prepare_map_aux.py with different configs in configs/dataset. For example:
```
python tools/prepare_map_aux.py +process=train
python tools/prepare_map_aux.py +process=val
```
You will have files like ./val_tmp.h5 and ./train_tmp.h5. You have to rename the cache files correctly after generating them. Our default is
```
data/nuscenes_map_aux
├── train_26x200x200_map_aux_full.h5 (42G)
└── val_26x200x200_map_aux_full.h5 (9G)
```

Train the model

Launch training with (with 8xV100):

accelerate launch --mixed_precision fp16 --gpu_ids all --num_processes 8 tools/train.py \
  +exp=224x400 runner=8gpus

During training, you can check tensorboard for the log and intermediate results.

Besides, we provides debug config to test your environment and data loading process (with 2xV100):

accelerate launch --mixed_precision fp16 --gpu_ids all --num_processes 2 tools/train.py \
  +exp=224x400 runner=debug runner.validation_before_run=true

Test the model

After training, you can test your model for driving view generation through:

python tools/test.py resume_from_checkpoint=${YOUR MODEL}
# take our pretrained model as an example
python tools/test.py resume_from_checkpoint=magicdrive-log/SDv1.5mv-rawbox_2023-09-07_18-39_224x400

Please find the results in ./magicdrive-log/test/.

Quantitative Results

Compare MagicDrive with other methods for generation quality:

Training support with images generated from MagicDrive:

More results can be found in the main paper.

Qualitative Results