# Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation

arXiv: https://arxiv.org/abs/2207.01878 · HTML: https://mlsanity.com/paper/2207.01878/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 34 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation](https://mlsanity.com/paper/2003.14032/) (2020)
- [Cross-view Transformers for real-time Map-view Semantic Segmentation](https://mlsanity.com/paper/2205.02833/) (2022)
- [FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras](https://mlsanity.com/paper/2104.10490/) (2021)
- [Orthographic Feature Transform for Monocular 3D Object Detection](https://mlsanity.com/paper/1811.08188/) (2018)
- [Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://mlsanity.com/paper/2003.13402/) (2020)
- [Cross-view Semantic Segmentation for Sensing Surroundings](https://mlsanity.com/paper/1906.03560/) (2019)
- [Translating Images into Maps](https://mlsanity.com/paper/2110.00966/) (2021)
- [Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation](https://mlsanity.com/paper/2008.01550/) (2020)
- [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)
- [Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer](https://mlsanity.com/paper/2206.04584/) (2022)
- [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)
- [Polar Parametrization for Vision-based Surround-View 3D Detection](https://mlsanity.com/paper/2206.10965/) (2022)
- [Learning to Look around Objects for Top-View Representations of Outdoor Scenes](https://mlsanity.com/paper/1803.10870/) (2018)
- [A Geometric Approach to Obtain a Bird's Eye View from an Image](https://mlsanity.com/paper/1905.02231/) (2019)
- [Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning](https://mlsanity.com/paper/2008.04047/) (2020)
- [A Parametric Top-View Representation of Complex Road Scenes](https://mlsanity.com/paper/1812.06152/) (2018)
- [Understanding Road Layout from Videos as a Whole](https://mlsanity.com/paper/2007.00822/) (2020)
- [AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception](https://mlsanity.com/paper/2203.13090/) (2022)
- ["The Pedestrian next to the Lamppost" Adaptive Object Graphs for Better Instantaneous Mapping](https://mlsanity.com/paper/2204.02944/) (2022)

## Built on by (most cited first, 23 of 23)

- [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023)
- [MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2208.14437/) (2022)
- [MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2308.05736/) (2023)
- [Symphonize 3D Semantic Scene Completion with Contextual Instance Queries](https://mlsanity.com/paper/2306.15670/) (2023)
- [Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction](https://mlsanity.com/paper/2303.08815/) (2023)
- [PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving](https://mlsanity.com/paper/2311.08100/) (2023)
- [Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction](https://mlsanity.com/paper/2212.02181/) (2022)
- [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2303.01212/) (2023)
- [LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation](https://mlsanity.com/paper/2304.11379/) (2023)
- [ADMap: Anti-disturbance framework for reconstructing online vectorized HD map](https://mlsanity.com/paper/2401.13172/) (2024)
- [VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene](https://mlsanity.com/paper/2304.09807/) (2023)
- [RayFormer: Improving Query-Based Multi-Camera 3D Object Detection via Ray-Centric Strategies](https://mlsanity.com/paper/2407.14923/) (2024)
- [Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation](https://mlsanity.com/paper/2308.14525/) (2023)
- [InteractionMap: Improving Online Vectorized HDMap Construction with Interaction](https://mlsanity.com/paper/2503.21659/) (2025)
- [PolarBEVDet: Exploring Polar Representation for Multi-View 3D Object Detection in Bird's-Eye-View](https://mlsanity.com/paper/2408.16200/) (2024)
- [Spatial Retrieval Augmented Autonomous Driving](https://mlsanity.com/paper/2512.06865/) (2025)
- [Learning High-resolution Vector Representation from Multi-Camera Images for 3D Object Detection](https://mlsanity.com/paper/2407.15354/) (2024)
- [One Training for Multiple Deployments: Polar-based Adaptive BEV Perception for Autonomous Driving](https://mlsanity.com/paper/2304.00525/) (2023)
- [Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back](https://mlsanity.com/paper/2505.18945/) (2025)
- [SynAD: Enhancing Real-World End-to-End Autonomous Driving Models through Synthetic Data Integration](https://mlsanity.com/paper/2510.24052/) (2025)
- [PetalView: Fine-grained Location and Orientation Extraction of Street-view Images via Cross-view Local Search with Supplementary Materials](https://mlsanity.com/paper/2406.13409/) (2024)
- [SPHERE: Semantic-PHysical Engaged REpresentation for 3D Semantic Scene Completion](https://mlsanity.com/paper/2509.11171/) (2025)
- [Multi-Resolution Alignment for Voxel Sparsity in Camera-Based 3D Semantic Scene Completion](https://mlsanity.com/paper/2602.03371/) (2026)

## Abstract (arXiv)

In this work, we propose PolarBEV for vision-based uneven BEV representation learning. To adapt to the foreshortening effect of camera imaging, we rasterize the BEV space both angularly and radially, and introduce polar embedding decomposition to model the associations among polar grids. Polar grids are rearranged to an array-like regular representation for efficient processing. Besides, to determine the 2D-to-3D correspondence, we iteratively update the BEV surface based on a hypothetical plane, and adopt height-based feature transformation. PolarBEV keeps real-time inference speed on a single 2080Ti GPU, and outperforms other methods for both BEV semantic segmentation and BEV instance segmentation. Thorough ablations are presented to validate the design. The code will be released at urlhttps://github.com/SuperZ-Liu/PolarBEV.