# Understanding Bird's-Eye View of Road Semantics using an Onboard Camera

arXiv: https://arxiv.org/abs/2012.03040 · HTML: https://mlsanity.com/paper/2012.03040/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

## Built on by (most cited first, 29 of 29)

- [Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?](https://mlsanity.com/paper/2206.07959/) (2022)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [Graph-based Topology Reasoning for Driving Scenes](https://mlsanity.com/paper/2304.05277/) (2023)
- [InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning](https://mlsanity.com/paper/2301.04470/) (2023)
- [SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation](https://mlsanity.com/paper/2211.15656/) (2022)
- [Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?](https://mlsanity.com/paper/2306.09281/) (2023)
- [MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert](https://mlsanity.com/paper/2412.12704/) (2024)
- [Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving](https://mlsanity.com/paper/2305.04205/) (2023)
- [U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based Relocalization](https://mlsanity.com/paper/2310.13766/) (2023)
- [NeMO: Neural Map Growing System for Spatiotemporal Fusion in Bird's-Eye-View and BDD-Map Benchmark](https://mlsanity.com/paper/2306.04540/) (2023)
- [Enhancing Online Road Network Perception and Reasoning with Standard Definition Maps](https://mlsanity.com/paper/2408.01471/) (2024)
- [Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation](https://mlsanity.com/paper/2308.14525/) (2023)
- [Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving](https://mlsanity.com/paper/2409.19560/) (2024)
- [TempBEV: Improving Learned BEV Encoders with Combined Image and BEV Space Temporal Aggregation](https://mlsanity.com/paper/2404.11803/) (2024)
- [Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory](https://mlsanity.com/paper/2501.01710/) (2025)
- [FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving](https://mlsanity.com/paper/2505.00318/) (2025)
- [Monocular BEV Perception of Road Scenes via Front-to-Top View Projection](https://mlsanity.com/paper/2211.08144/) (2022)
- [ChatBEV: Empowering Traffic Scene Understanding and Simulation via Vision-Language Model](https://mlsanity.com/paper/2503.13938/) (2025)
- [One Training for Multiple Deployments: Polar-based Adaptive BEV Perception for Autonomous Driving](https://mlsanity.com/paper/2304.00525/) (2023)
- [Efficient Baselines for Motion Prediction in Autonomous Driving](https://mlsanity.com/paper/2309.03387/) (2023)
- [BEVCon: Advancing Bird's Eye View Perception with Contrastive Learning](https://mlsanity.com/paper/2508.04702/) (2025)
- [Focus on BEV: Self-calibrated Cycle View Transformation for Monocular Birds-Eye-View Segmentation](https://mlsanity.com/paper/2410.15932/) (2024)
- [Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction](https://mlsanity.com/paper/2205.13071/) (2022)
- [GSDC Transformer: An Efficient and Effective Cue Fusion for Monocular Multi-Frame Depth Estimation](https://mlsanity.com/paper/2309.17059/) (2023)
- [Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator](https://mlsanity.com/paper/2502.02972/) (2025)
- [Online Camera-to-ground Calibration for Autonomous Driving](https://mlsanity.com/paper/2303.17137/) (2023)
- [Online Lane Graph Extraction from Onboard Video](https://mlsanity.com/paper/2304.00930/) (2023)
- [Roundabout Dilemma Zone Data Mining and Forecasting with Trajectory Prediction and Graph Neural Networks](https://mlsanity.com/paper/2409.00622/) (2024)

## Abstract (arXiv)

Autonomous navigation requires scene understanding of the action-space to move or anticipate events. For planner agents moving on the ground plane, such as autonomous vehicles, this translates to scene understanding in the bird's-eye view (BEV). However, the onboard cameras of autonomous cars are customarily mounted horizontally for a better view of the surrounding. In this work, we study scene understanding in the form of online estimation of semantic BEV maps using the video input from a single onboard camera. We study three key aspects of this task, image-level understanding, BEV level understanding, and the aggregation of temporal information. Based on these three pillars we propose a novel architecture that combines these three aspects. In our extensive experiments, we demonstrate that the considered aspects are complementary to each other for BEV understanding. Furthermore, the proposed architecture significantly surpasses the current state-of-the-art. Code: https://github.com/ybarancan/BEV_feat_stitch.