# Deep Perspective Transformation Based Vehicle Localization on Bird's Eye View

arXiv: https://arxiv.org/abs/2311.06796 · HTML: https://mlsanity.com/paper/2311.06796/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 12 Nov 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://mlsanity.com/paper/2003.13402/) (2020)
- [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [Translating Images into Maps](https://mlsanity.com/paper/2110.00966/) (2021)
- [Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images](https://mlsanity.com/paper/2110.01997/) (2021)
- [Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks](https://mlsanity.com/paper/1804.02176/) (2018)
- [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](https://mlsanity.com/paper/2005.04078/) (2020)
- [FISHING Net: Future Inference of Semantic Heatmaps In Grids](https://mlsanity.com/paper/2006.09917/) (2020)
- [Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images](https://mlsanity.com/paper/2108.03227/) (2021)
- [Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems](https://mlsanity.com/paper/1809.07408/) (2018)
- [Generative Adversarial Frontal View to Bird View Synthesis](https://mlsanity.com/paper/1808.00327/) (2018)
- [Homography Loss for Monocular 3D Object Detection](https://mlsanity.com/paper/2204.00754/) (2022)
- [Learning to Map Vehicles into Bird's Eye View](https://mlsanity.com/paper/1706.08442/) (2017)
- [Short-Term Prediction and Multi-Camera Fusion on Semantic Grids](https://mlsanity.com/paper/1903.08960/) (2019)
- [Autonomous Vehicles: Open-Source Technologies, Considerations, and Development](https://mlsanity.com/paper/2202.03148/) (2022)

## Abstract (arXiv)

An accurate understanding of a self-driving vehicle's surrounding environment is crucial for its navigation system. To enhance the effectiveness of existing algorithms and facilitate further research, it is essential to provide comprehensive data to the routing system. Traditional approaches rely on installing multiple sensors to simulate the environment, leading to high costs and complexity. In this paper, we propose an alternative solution by generating a top-down representation of the scene, enabling the extraction of distances and directions of other cars relative to the ego vehicle. We introduce a new synthesized dataset that offers extensive information about the ego vehicle and its environment in each frame, providing valuable resources for similar downstream tasks. Additionally, we present an architecture that transforms perspective view RGB images into bird's-eye-view maps with segmented surrounding vehicles. This approach offers an efficient and cost-effective method for capturing crucial environmental information for self-driving cars. Code and dataset are available at https://github.com/IPM-HPC/Perspective-BEV-Transformer.