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TensorFlow Implementation for Computing a Semantically Segmented Bird's Eye View (BEV) Image Given the Images of Multiple Vehicle-Mounted Cameras.
| Date | Stars |
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| 2026-07-24 | 790 |
| 2026-07-25 | 790 |
| 2026-07-28 | 790 |
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# <img src="assets/logo.png" width=50> Cam2BEV <img src="assets/teaser.gif" align="right" width=320 height=200> This repository contains the official implementation of our methodology for the computation of a semantically segmented bird's eye view (BEV) image given the images of multiple vehicle-mounted cameras as presented in our paper: > **A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird’s Eye View** ([IEEE Xplore](https://ieeexplore.ieee.org/document/9294462), [arXiv](https://arxiv.org/abs/2005.04078)) > > [Lennart Reiher](https://github.com/lreiher), [Bastian Lampe](https://github.com/bastilam), and [Lutz Eckstein](https://www.ika.rwth-aachen.de/en/institute/team/univ-prof-dr-ing-lutz-eckstein.html) > [Institute for Automotive Engineering (ika), RWTH Aachen University](https://www.ika.rwth-aachen.de/en/) > [!IMPORTANT] > This repository is open-sourced and maintained by the [**Institute for Automotive Engineering (ika) at RWTH Aachen University**](https://www.ika.rwth-aachen.de/). > **Deep Learning-based Perception** is one of many research topics within our [*Vehicle Intelligence & Automated Driving*](https://www.ika.rwth-aachen.de/en/competences/fields-of-research/vehicle-intelligence-automated-driving.html) domain. > If you would like to learn more about how we can support your advanced driver assistance and automated driving efforts, feel free to reach out to us! > :email: ***[email protected]*** [](https://youtu.be/TzXuwt56a0E) > _**Abstract**_ — Accurate environment perception is essential for automated driving. When using monocular cameras, the distance estimation of elements in the environment poses a major challenge. Distances can be more easily estimated when the camera perspective is transformed to a bird's eye view (BEV). For flat surfaces, _Inverse Perspective Mapping_ (IPM) can accurately transform images to a BEV. Three-dimensional objects such as vehicles and vulnerable road users are distorted by this transformation making it difficult to estimate their position relative to the sensor. This paper describes a methodology to obtain a corrected 360° BEV image given images from multiple vehicle-mounted cameras. The corrected BEV image is segmented into semantic classes and includes a prediction of occluded areas. The neural network approach does not rely on manually labeled data, but is trained on a synthetic dataset in such a way that it generalizes well to real-world data. By using semantically segmented images as input, we reduce the reality gap between simulated and real-world data and are able to show that our method can be successfully applied in the real world. Extensive experiments conducted on the synthetic data demonstrate the superiority of our approach compared to IPM. We hope our paper, data and code can help in your research. If this is the case, please cite: ``` @INPROCEEDINGS{ReiherLampe2020Cam2BEV, author={L. {Reiher} and B. {Lampe} and L. {Eckstein}}, booktitle={2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)}, title={A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird’s Eye View}, year={2020}, doi={10.1109/ITSC45102.2020.9294462}} ``` ## Content - [Repository Structure](#repository-structure) - [Installation](#installation) - [Data](#data) - [Preprocessing](#preprocessing) - [Training](#training) - [Neural Network Architectures](#neural-network-architectures) - [Customization](#customization) ## Repository Structure ``` Cam2BEV ├── data # where our synthetic datasets are downloaded to by default ├── model # training scripts and configurations │ ├── architecture # TensorFlow implementations of neural network architec
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matched fp:f670cfcd9beb9bdd, topic:deep-learning
matched fp:f670cfcd9beb9bdd, topic:computer-vision
matched fp:f670cfcd9beb9bdd, topic:simulation