# Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving

arXiv: https://arxiv.org/abs/2305.04205 · HTML: https://mlsanity.com/paper/2305.04205/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 43 references that are in the map.
- First posted on arXiv 7 May 2023.

## Builds on (most cited first, 30 of 32)

- [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)
- [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)
- [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning](https://mlsanity.com/paper/2207.07601/) (2022)
- [MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2208.14437/) (2022)
- [HDMapNet: An Online HD Map Construction and Evaluation Framework](https://mlsanity.com/paper/2107.06307/) (2021)
- [Cross-view Transformers for real-time Map-view Semantic Segmentation](https://mlsanity.com/paper/2205.02833/) (2022)
- [VectorMapNet: End-to-end Vectorized HD Map Learning](https://mlsanity.com/paper/2206.08920/) (2022)
- [FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras](https://mlsanity.com/paper/2104.10490/) (2021)
- [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)
- [M2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation](https://mlsanity.com/paper/2204.05088/) (2022)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs](https://mlsanity.com/paper/2203.04050/) (2022)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [Translating Images into Maps](https://mlsanity.com/paper/2110.00966/) (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)
- [BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2211.09386/) (2022)
- [Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images](https://mlsanity.com/paper/2108.03227/) (2021)
- [Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2211.07171/) (2022)
- [Feature Fusion for Online Mutual Knowledge Distillation](https://mlsanity.com/paper/1904.09058/) (2019)
- [A Geometric Approach to Obtain a Bird's Eye View from an Image](https://mlsanity.com/paper/1905.02231/) (2019)
- [Understanding Bird's-Eye View of Road Semantics using an Onboard Camera](https://mlsanity.com/paper/2012.03040/) (2020)
- [GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation](https://mlsanity.com/paper/2204.07733/) (2022)
- [MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding](https://mlsanity.com/paper/2107.00346/) (2021)

## Built on by (most cited first, 14 of 14)

- [Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps](https://mlsanity.com/paper/2311.04079/) (2023)
- [PointBeV: A Sparse Approach to BeV Predictions](https://mlsanity.com/paper/2312.00703/) (2023)
- [InsMapper: Exploring Inner-instance Information for Vectorized HD Mapping](https://mlsanity.com/paper/2308.08543/) (2023)
- [CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity](https://mlsanity.com/paper/2310.02815/) (2023)
- [OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks](https://mlsanity.com/paper/2404.14027/) (2024)
- [DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction](https://mlsanity.com/paper/2405.05518/) (2024)
- [PriorMapNet: Enhancing Online Vectorized HD Map Construction with Priors](https://mlsanity.com/paper/2408.08802/) (2024)
- [Improving Hierarchical Representations of Vectorized HD Maps with Perspective Clues](https://mlsanity.com/paper/2404.11155/) (2024)
- [BEVNav: Robot Autonomous Navigation Via Spatial-Temporal Contrastive Learning in Bird's-Eye View](https://mlsanity.com/paper/2409.01646/) (2024)
- [An Overview about Emerging Technologies of Autonomous Driving](https://mlsanity.com/paper/2306.13302/) (2023)
- [Inferring Driving Maps by Deep Learning-based Trail Map Extraction](https://mlsanity.com/paper/2505.10258/) (2025)
- [Failure Modes for Deep Learning-Based Online Mapping: How to Measure and Address Them](https://mlsanity.com/paper/2603.19852/) (2026)
- [Cross-modal Context-aware Learning for Visual Prompt Guided Multimodal Image Understanding in Remote Sensing](https://mlsanity.com/paper/2512.11680/) (2025)
- [Scene Graph-guided SegCaptioning Transformer with Fine-grained Alignment for Controllable Video Segmentation and Captioning](https://mlsanity.com/paper/2603.20887/) (2026)

## Abstract (arXiv)

A semantic map of the road scene, covering fundamental road elements, is an essential ingredient in autonomous driving systems. It provides important perception foundations for positioning and planning when rendered in the Bird's-Eye-View (BEV). Currently, the prior knowledge of hypothetical depth can guide the learning of translating front perspective views into BEV directly with the help of calibration parameters. However, it suffers from geometric distortions in the representation of distant objects. In addition, another stream of methods without prior knowledge can learn the transformation between front perspective views and BEV implicitly with a global view. Considering that the fusion of different learning methods may bring surprising beneficial effects, we propose a Bi-Mapper framework for top-down road-scene semantic understanding, which incorporates a global view and local prior knowledge. To enhance reliable interaction between them, an asynchronous mutual learning strategy is proposed. At the same time, an Across-Space Loss (ASL) is designed to mitigate the negative impact of geometric distortions. Extensive results on nuScenes and Cam2BEV datasets verify the consistent effectiveness of each module in the proposed Bi-Mapper framework. Compared with exiting road mapping networks, the proposed Bi-Mapper achieves 2.1% higher IoU on the nuScenes dataset. Moreover, we verify the generalization performance of Bi-Mapper in a real-world driving scenario. The source code is publicly available at https://github.com/lynn-yu/Bi-Mapper.