# Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?

arXiv: https://arxiv.org/abs/2206.07959 · HTML: https://mlsanity.com/paper/2206.07959/

## Key facts

- Cited by 128 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 47 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [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)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [DeepVoxels: Learning Persistent 3D Feature Embeddings](https://mlsanity.com/paper/1812.01024/) (2018)
- [Categorical Depth Distribution Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.01100/) (2021)
- [Don't Decay the Learning Rate, Increase the Batch Size](https://mlsanity.com/paper/1711.00489/) (2017)
- [Cross-view Transformers for real-time Map-view Semantic Segmentation](https://mlsanity.com/paper/2205.02833/) (2022)
- [Is Pseudo-Lidar needed for Monocular 3D Object detection?](https://mlsanity.com/paper/2108.06417/) (2021)
- [FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras](https://mlsanity.com/paper/2104.10490/) (2021)
- [Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis](https://mlsanity.com/paper/1701.02096/) (2017)
- [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)
- [Multimodal Virtual Point 3D Detection](https://mlsanity.com/paper/2111.06881/) (2021)
- [Translating Images into Maps](https://mlsanity.com/paper/2110.00966/) (2021)
- [FISHING Net: Future Inference of Semantic Heatmaps In Grids](https://mlsanity.com/paper/2006.09917/) (2020)
- [RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects](https://mlsanity.com/paper/2007.14366/) (2020)
- [Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images](https://mlsanity.com/paper/2108.03227/) (2021)
- [Learning to Look around Objects for Top-View Representations of Outdoor Scenes](https://mlsanity.com/paper/1803.10870/) (2018)
- [Learning Spatial Common Sense with Geometry-Aware Recurrent Networks](https://mlsanity.com/paper/1901.00003/) (2018)
- [Understanding Bird's-Eye View of Road Semantics using an Onboard Camera](https://mlsanity.com/paper/2012.03040/) (2020)
- [Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation](https://mlsanity.com/paper/2104.12861/) (2021)
- [Exploiting Temporal Relations on Radar Perception for Autonomous Driving](https://mlsanity.com/paper/2204.01184/) (2022)
- [Geometry-Aware Recurrent Neural Networks for Active Visual Recognition](https://mlsanity.com/paper/1811.01292/) (2018)

## Built on by (most cited first, 30 of 128)

- [BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision](https://mlsanity.com/paper/2211.10439/) (2022)
- [CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception](https://mlsanity.com/paper/2304.00670/) (2023)
- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion](https://mlsanity.com/paper/2307.00724/) (2023)
- [FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D Bird's-Eye View and Perspective View](https://mlsanity.com/paper/2403.02710/) (2024)
- [Localization Is All You Evaluate: Data Leakage in Online Mapping Datasets and How to Fix It](https://mlsanity.com/paper/2312.06420/) (2023)
- [OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments](https://mlsanity.com/paper/2312.09243/) (2023)
- [Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision](https://mlsanity.com/paper/2504.02477/) (2025)
- [SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation](https://mlsanity.com/paper/2211.15656/) (2022)
- [SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding](https://mlsanity.com/paper/2306.05407/) (2023)
- [Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception](https://mlsanity.com/paper/2403.07746/) (2024)
- [EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View](https://mlsanity.com/paper/2310.13350/) (2023)
- [OccFusion: Multi-Sensor Fusion Framework for 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2403.01644/) (2024)
- [BEVCar: Camera-Radar Fusion for BEV Map and Object Segmentation](https://mlsanity.com/paper/2403.11761/) (2024)
- [RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving](https://mlsanity.com/paper/2402.19341/) (2024)
- [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2303.01212/) (2023)
- [Lifting Multi-View Detection and Tracking to the Bird's Eye View](https://mlsanity.com/paper/2403.12573/) (2024)
- [PointBeV: A Sparse Approach to BeV Predictions](https://mlsanity.com/paper/2312.00703/) (2023)
- [Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?](https://mlsanity.com/paper/2306.09281/) (2023)
- [TEMPO: Efficient Multi-View Pose Estimation, Tracking, and Forecasting](https://mlsanity.com/paper/2309.07910/) (2023)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception](https://mlsanity.com/paper/2501.15394/) (2025)
- [JAFAR: Jack up Any Feature at Any Resolution](https://mlsanity.com/paper/2506.11136/) (2025)
- [GaussianBeV: 3D Gaussian Representation meets Perception Models for BeV Segmentation](https://mlsanity.com/paper/2407.14108/) (2024)
- [Toward Real-world BEV Perception: Depth Uncertainty Estimation via Gaussian Splatting](https://mlsanity.com/paper/2504.01957/) (2025)
- [MV-Map: Offboard HD-Map Generation with Multi-view Consistency](https://mlsanity.com/paper/2305.08851/) (2023)
- [DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection](https://mlsanity.com/paper/2404.03015/) (2024)
- [S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation](https://mlsanity.com/paper/2505.24139/) (2025)
- [LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation](https://mlsanity.com/paper/2304.11379/) (2023)
- [GaussRender: Learning 3D Occupancy with Gaussian Rendering](https://mlsanity.com/paper/2502.05040/) (2025)

## Abstract (arXiv)

Building 3D perception systems for autonomous vehicles that do not rely on high-density LiDAR is a critical research problem because of the expense of LiDAR systems compared to cameras and other sensors. Recent research has developed a variety of camera-only methods, where features are differentiably "lifted" from the multi-camera images onto the 2D ground plane, yielding a "bird's eye view" (BEV) feature representation of the 3D space around the vehicle. This line of work has produced a variety of novel "lifting" methods, but we observe that other details in the training setups have shifted at the same time, making it unclear what really matters in top-performing methods. We also observe that using cameras alone is not a real-world constraint, considering that additional sensors like radar have been integrated into real vehicles for years already. In this paper, we first of all attempt to elucidate the high-impact factors in the design and training protocol of BEV perception models. We find that batch size and input resolution greatly affect performance, while lifting strategies have a more modest effect -- even a simple parameter-free lifter works well. Second, we demonstrate that radar data can provide a substantial boost to performance, helping to close the gap between camera-only and LiDAR-enabled systems. We analyze the radar usage details that lead to good performance, and invite the community to re-consider this commonly-neglected part of the sensor platform.