This repository contains the official implementation of our ICCV 2021 paper MGNet: Monocular Geometric Scene Understanding for Autonomous Driving.
This is a re-implementation based on detectron2, hence results differ slightly compared to the ones reported in the paper.
See INSTALL.md for instructions on how to prepare your environment to use MGNet.
See datasets/README.md for instructions on how to prepare datasets for MGNet.
See GETTING_STARTED.md for instructions on how to train and evaluate models, or run inference on demo images.
See trt_inference/README.md for instructions on how to export trained models to TensorRT and run optimized inference.
All models were trained using 4 NVIDIA 2080Ti GPUs.
| Name | PQ | PQ_St | PQ_Th | Abs Rel | RMSE | δ < 1.25 | Download |
|---|---|---|---|---|---|---|---|
| MGNet Cityscapes Fine | 54.879 | 62.524 | 44.367 | 0.188 | 8.439 | 0.744 | model |
| MGNet Cityscapes Video Sequence | 55.644 | 63.140 | 45.337 | 0.166 | 7.984 | 0.794 | model |
| MGNet KITTI Eigen Zhou | - | - | - | 0.095 | 3.788 | 0.897 | model |
Please use the following citations when referencing our work:
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving (ICCV 2021)
Markus Schön, Michael Buchholz and Klaus Dietmayer, [paper], [video]
@InProceedings{Schoen_2021_ICCV,
author = {Sch{\"o}n, Markus and Buchholz, Michael and Dietmayer, Klaus},
title = {MGNet: Monocular Geometric Scene Understanding for Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {15804-15815}
}
We used and modified code parts from other open source projects, we especially like to thank the authors of:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent