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https://github.com/4DVLab/Vision-Centric-BEV-Perception

Vision-Centric BEV Perception: A Survey
https://github.com/4DVLab/Vision-Centric-BEV-Perception
bev-perception bird-eye-view deep-learning transformer vision-transformer
Last synced: 5 days ago
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Vision-Centric BEV Perception: A Survey
Host: GitHub
URL: https://github.com/4DVLab/Vision-Centric-BEV-Perception
Owner: 4DVLab
Created: 2022-07-26T07:44:29.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2023-09-03T03:32:22.000Z (10 months ago)
Last Synced: 2024-03-12T21:32:48.464Z (4 months ago)
Topics: bev-perception, bird-eye-view, deep-learning, transformer, vision-transformer
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Watchers: 29
Forks: 66
Open Issues: 0
Metadata Files:
- Readme: README.md
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awesome-stars - 4DVLab/Vision-Centric-BEV-Perception - Vision-Centric BEV Perception: A Survey (Others)
README

        # Vision-Centric-BEV-Perception

Vision-Centric BEV Perception: A Survey

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![](BEV_Survey/taxonomy_bev.PNG)

## Introduction

### (1) Datasets

![](BEV_Survey/Datasets_bev.png)

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### (2) GEOMETRY BASED PV2BEV

#### Homograph based PV2BEV

Public Papers:

- IPM: Inverse perspective mapping simplifies optical flow computation and obstacle detection (Biological Cybernetics'1991) [[paper]](https://link.springer.com/article/10.1007/BF00201978)

- DSM: Automatic Dense Visual Semantic Mapping from Street-Level Imagery (IROS'12) [[paper]](https://www.robots.ox.ac.uk/~tvg/publications/2012/IROS_Mapping_ss.pdf)

- MapV: Learning to map vehicles into bird’s eye view (ICIAP'17) [[paper]](https://arxiv.org/abs/1706.08442)

- BridgeGAN: Generative Adversarial Frontal View to Bird View Synthesis (3DV'18) [[paper]](https://arxiv.org/pdf/1808.00327.pdf)[[project page]](https://github.com/xinge008/BridgeGAN)

- VPOE: Deep learning based vehicle position and orientation estimation via inverse perspective mapping image (IV'19) [[paper]](https://ieeexplore.ieee.org/document/8814050)

- 3D-LaneNet: End-to-End 3D Multiple Lane Detection (ICCV'19) [[paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Garnett_3D-LaneNet_End-to-End_3D_Multiple_Lane_Detection_ICCV_2019_paper.pdf)

- The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping (IV'19) [[paper]](https://arxiv.org/abs/1812.00913)

- Cam2BEV: 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 (ITSC'20) [[paper]](https://arxiv.org/abs/2005.04078) [[project page]](https://github.com/ika-rwth-aachen/Cam2BEV)

- MonoLayout: Amodal Scene Layout from a Single Image (WACA'20) [[paper]](https://arxiv.org/abs/2002.08394) [[project page]](https://github.com/hbutsuak95/monolayout)

- MVNet: Multiview Detection with Feature Perspective Transformation (ECCV'20) [[paper]](https://arxiv.org/abs/2007.07247) [[project page]](https://github.com/hou-yz/MVDet)

- OGMs: Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning (WACA'21) [[paper]](https://arxiv.org/abs/2008.04047) [[project page]]()

- TrafCam3D: Monocular 3D Vehicle Detection Using Uncalibrated Traffic Camerasthrough Homography (IROS'21) [[paper]](https://arxiv.org/pdf/2103.15293.pdf) [[project page]](https://github.com/minghanz/trafcam_3d)

- SHOT:Stacked Homography Transformations for Multi-View Pedestrian Detection (ICCV'21) [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Song_Stacked_Homography_Transformations_for_Multi-View_Pedestrian_Detection_ICCV_2021_paper.pdf)

- HomoLoss: Homography Loss for Monocular 3D Object Detection (CVPR'22) [[paper]](https://arxiv.org/abs/2204.00754)

Chronological Overview:

![](BEV_Survey/homo-based-overview.PNG)

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#### Depth based PV2BEV

Public Papers:

- OFT: Orthographic Feature Transform for Monocular 3D Object Detection (BMVC'19) [[paper]](https://arxiv.org/pdf/1811.08188) [[project page]](https://github.com/tom-roddick/oft)

- CaDDN: Categorical Depth Distribution Network for Monocular 3D Object Detection (CVPR'21) [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Reading_Categorical_Depth_Distribution_Network_for_Monocular_3D_Object_Detection_CVPR_2021_paper.pdf) [[project page]](https://github.com/TRAILab/CaDDN)

- DSGN: Deep Stereo Geometry Network for 3D Object Detection (CVPR'20) [[paper]](https://arxiv.org/pdf/2001.03398) [[project page]](https://github.com/dvlab-research/DSGN)

- Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D (ECCV'20) [[paper]](https://arxiv.org/pdf/2008.05711.pdf) [[project page]](https://nv-tlabs.github.io/lift-splat-shoot/)

- PanopticSeg: Bird’s-Eye-View Panoptic Segmentation Using Monocular Frontal View Images (RA-L'22) [[paper]](https://ieeexplore.ieee.org/document/9681287) [[project page]](http://panoptic-bev.cs.uni-freiburg.de/)

- FIERY: Future Instance Prediction in Bird’s-Eye View from Surround Monocular Cameras (ICCV'21) [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Hu_FIERY_Future_Instance_Prediction_in_Birds-Eye_View_From_Surround_Monocular_ICCV_2021_paper.pdf) [[project page]](https://github.com/wayveai/fiery)

- LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector (ICCV'21) [[paper]](https://xy-guo.github.io/liga/liga-guo-iccv21.pdf) [[project page]](https://xy-guo.github.io/liga/)

- ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection (WACV'22) [[paper]](https://arxiv.org/pdf/2106.01178.pdf) [[project page]](https://github.com/saic-vul/imvoxelnet)

- BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View (Arxiv'21) [[paper]](https://arxiv.org/pdf/2112.11790) [[project page]](https://github.com/HuangJunJie2017/BEVDet)

- M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Bird’s-Eye View Representation (Arxiv'22) [[paper]](https://arxiv.org/pdf/2204.05088) [[project page]](https://nvlabs.github.io/M2BEV/)

- StretchBEV: Stretching Future Instance Prediction Spatially and Temporally (ECCV'22) [[paper]](https://arxiv.org/pdf/2203.13641) [[project page]](https://github.com/kaanakan/stretchbev)

- DfM: Monocular 3D Object Detection with Depth from Motion (ECCV'22) [[paper]](https://arxiv.org/pdf/2207.12988.pdf) [[project page]](https://github.com/Tai-Wang/Depth-from-Motion)

- BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection (Arxiv'22) [[paper]](https://arxiv.org/pdf/2203.17054) [[project page]](https://github.com/HuangJunJie2017/BEVDet)

- BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving (Arxiv'22) [[paper]](https://arxiv.org/pdf/2205.09743) [[project page]](https://github.com/zhangyp15/BEVerse)

- MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones (Arxiv'22) [[paper]](https://arxiv.org/pdf/2207.12716) [[project page]](https://github.com/Tai-Wang/Depth-from-Motion)

- Putting People in their Place: Monocular Regression of 3D People in Depth (CVPR'22) [[Code]](https://github.com/Arthur151/ROMP) [[Project Page]](https://arthur151.github.io/BEV/BEV.html) [[Paper]](https://arxiv.org/abs/2112.08274) [[Video]](https://youtu.be/Q62fj_6AxRI) [[RH Dataset]](https://github.com/Arthur151/Relative_Human)

Chronological Overview:

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![](BEV_Survey/depth-based-overview.png)

Benchmark Results:

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![](BEV_Survey/depth-based%20results.png)

### (3) NETWORK BASED PV2BEV

#### MLP based PV2BEV

Public Papers:

- VED: Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks (RA-L'19) [[paper]](https://arxiv.org/pdf/1804.02176.pdf) [[project page]](https://github.com/Chenyang-Lu/mono-semantic-occupancy)

- VPN: Cross-view Semantic Segmentation for Sensing Surroundings (IROS'20) [[paper]](https://arxiv.org/pdf/1906.03560.pdf) [[project page]](https://view-parsing-network.github.io/)

- FishingNet: Future Inference of Semantic Heatmaps In Grids (Arxiv'20) [[paper]](https://arxiv.org/pdf/2006.09917)

- PON: Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks (CVPR'20) [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Roddick_Predicting_Semantic_Map_Representations_From_Images_Using_Pyramid_Occupancy_Networks_CVPR_2020_paper.pdf) [[project page]](https://github.com/tom-roddick/mono-semantic-maps)

- STA-ST: Enabling spatio-temporal aggregation in Birds-Eye-View Vehicle Estimation (ICRA'21) [[paper]](https://cvssp.org/Personal/OscarMendez/papers/pdf/SahaICRA2021.pdf) 

- HDMapNet: An Online HD Map Construction and Evaluation Framework (ICRA'22) [[paper]](https://arxiv.org/pdf/2107.06307) [[project page]](https://tsinghua-mars-lab.github.io/HDMapNet/)

- Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation (CVPR'21) [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Projecting_Your_View_Attentively_Monocular_Road_Scene_Layout_Estimation_via_CVPR_2021_paper.pdf) [[project page]](https://github.com/JonDoe-297/cross-view)

- HFT: Lifting Perspective Representations via Hybrid Feature Transformation (Arxiv'22) [[paper]](https://arxiv.org/pdf/2204.05068) [[project page]](https://github.com/JiayuZou2020/HFT)

Chronological Overview:

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![](BEV_Survey/MLP-based-overview1.png)

Benchmark Results:

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![](BEV_Survey/MLP-based-result.png)

#### Transformer based PV2BEV

Public Papers:

- STSU: Structured Bird’s-Eye-View Traffic Scene Understanding from Onboard Images (ICCV'21) [[paper]](https://arxiv.org/pdf/2110.01997) [[project page]](https://github.com/ybarancan/STSU)

- Image2Map: Translating Images into Maps (ICRA'22) [[paper]](https://arxiv.org/pdf/2110.00966.pdf) [[project page]](https://github.com/avishkarsaha/translating-images-into-maps)

- DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries (CoRL'21) [[paper]](https://arxiv.org/pdf/2110.06922.pdf) [[project page]](https://github.com/WangYueFt/detr3d)

- TopologyPL: Topology Preserving Local Road Network Estimation from Single Onboard Camera Image (CVPR'22) [[paper]](https://arxiv.org/pdf/2112.10155.pdf) [[project page]](https://github.com/ybarancan/TopologicalLaneGraph)

- PETR: Position Embedding Transformation for Multi-View 3D Object Detection (ECCV'22) [[paper]](https://arxiv.org/pdf/2203.05625) [[project page]](https://github.com/megvii-research/PETR)

- BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs (Arxiv'22) [[paper]](https://arxiv.org/pdf/2203.04050) 

- PersFormer: a New Baseline for 3D Laneline Detection (ECCV'22) [[paper]](https://arxiv.org/pdf/2203.11089) [[project page]](https://github.com/OpenPerceptionX/PersFormer_3DLane)

- MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer (CVPR'22) [[page]](https://arxiv.org/pdf/2203.10981) [[project page]](https://github.com/kuanchihhuang/MonoDTR)

- MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection (Arxiv'22) [[paper]](https://arxiv.org/pdf/2203.13310.pdf) [[project page]](https://github.com/ZrrSkywalker/MonoDETR)

- BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers (ECCV'22) [[paper]](https://arxiv.org/pdf/2203.17270v1.pdf) [[project page]](https://github.com/zhiqi-li/BEVFormer)

- GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation (ECCV'22) [[paper]](https://arxiv.org/pdf/2204.07733) 

- Graph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object Detection (MM'22) [[paper]](https://arxiv.org/pdf/2204.11582) 

- CVT: Cross-view Transformers for real-time Map-view Semantic Segmentation (CVPR'22) [[paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Cross-View_Transformers_for_Real-Time_Map-View_Semantic_Segmentation_CVPR_2022_paper.pdf) [[project page]](https://github.com/bradyz/)

- PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images (Arxiv'22) [[paper]](https://arxiv.org/pdf/2206.01256) [[project page]](https://github.com/megvii-research/PETR)

- Ego3RT: Learning Ego 3D Representation as Ray Tracing (ECCV'22) [[paper]](https://arxiv.org/pdf/2206.04042.pdf) [[project page]](https://github.com/fudan-zvg/Ego3RT)

- GKT: Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer (Arxiv'22) [[paper]](https://arxiv.org/pdf/2206.04584.pdf) [[project page]](https://github.com/hustvl/GKT)

- PolarDETR: Polar Parametrization for Vision-based Surround-View 3D Detection (Arxiv'22) [[paper]](https://arxiv.org/pdf/2206.10965) [[project page]](https://github.com/hustvl/PolarDETR)

- LaRa: Latents and Rays for Multi-Camera Bird’s-Eye-View Semantic Segmentation (Arxiv'22) [[paper]](https://arxiv.org/pdf/2206.13294)

- SRCN3D: Sparse R-CNN 3D Surround-View Cameras 3D Object Detection and Tracking for Autonomous Driving (Arxiv'22) [[paper]](https://arxiv.org/pdf/2206.14451) [[project page]](https://github.com/synsin0/SRCN3D)

- PolarFormer: Multi-camera 3D Object Detection with Polar Transformers (Arxiv'22)[[paper]](https://arxiv.org/pdf/2206.15398) [[project page]](https://github.com/fudan-zvg/PolarFormer)

- ORA3D: ORA3D: Overlap Region Aware Multi-view 3D Object Detection (Arxiv'22) [[paper]](https://arxiv.org/pdf/2207.00865)

- CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers (Arxiv'22)  [[paper]](https://arxiv.org/pdf/2207.02202.pdf)

 

Chronological Overview:

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![](BEV_Survey/transformer-based-overview.png)

Benchmark Results:

![](BEV_Survey/transformer-based-results.png)

![](BEV_Survey/transformer-based-results1.png)

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### (4)  EXTENSION

#### Multi-Task Learning under BEV

- FIERY: Future Instance Prediction in Bird’s-Eye View from Surround Monocular Cameras (ICCV'21) [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Hu_FIERY_Future_Instance_Prediction_in_Birds-Eye_View_From_Surround_Monocular_ICCV_2021_paper.pdf) [[project page]](https://github.com/wayveai/fiery)

- StretchBEV: Stretching Future Instance Prediction Spatially and Temporally (ECCV'22) [[paper]](https://arxiv.org/pdf/2203.13641) [[project page]](https://github.com/kaanakan/stretchbev)

- BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving (Arxiv'22) [[paper]](https://arxiv.org/pdf/2205.09743) [[project page]](https://github.com/zhangyp15/BEVerse)

- M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Bird’s-Eye View Representation (Arxiv'22) [[paper]](https://arxiv.org/pdf/2204.05088) [[project page]](https://nvlabs.github.io/M2BEV/)

- STSU: Structured Bird’s-Eye-View Traffic Scene Understanding from Onboard Images (ICCV'21) [[paper]](https://arxiv.org/pdf/2110.01997) [[project page]](https://github.com/ybarancan/STSU)

- BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers (ECCV'22) [[paper]](https://arxiv.org/pdf/2203.17270v1.pdf) [[project page]](https://github.com/zhiqi-li/BEVFormer)

- Ego3RT: Learning Ego 3D Representation as Ray Tracing (ECCV'22) [[paper]](https://arxiv.org/pdf/2206.04042.pdf) [[project page]](https://github.com/fudan-zvg/Ego3RT)

- PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images (Arxiv'22) [[paper]](https://arxiv.org/pdf/2206.01256) [[project page]](https://github.com/megvii-research/PETR)

- PolarFormer: Multi-camera 3D Object Detection with Polar Transformers (Arxiv'22)[[paper]](https://arxiv.org/pdf/2206.15398) [[project page]](https://github.com/fudan-zvg/PolarFormer)

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![](BEV_Survey/multi-task-results.png)

#### Fusion under BEV

Multi-Modality Fusion:

- PointPainting: Sequential Fusion for 3D Object Detection (CVPR'19) [[paper]](https://arxiv.org/pdf/1911.10150.pdf) [[project page]](https://github.com/AmrElsersy/PointPainting)

- 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection (ECCV'20) [[paper]](https://arxiv.org/pdf/2004.12636.pdf) [[project page]](https://github.com/rasd3/3D-CVF)

- FUTR3D: A Unified Sensor Fusion Framework for 3D Detection (Arxiv'22) [[paper]](https://arxiv.org/pdf/2203.10642) [[project page]](https://github.com/Tsinghua-MARS-Lab/futr3d)

- MVP: Multimodal Virtual Point 3D Detection (NIPS'21) [[paper]](https://arxiv.org/pdf/2111.06881.pdf) [[project page]](https://tianweiy.github.io/mvp/)

- PointAugmenting: Cross-Modal Augmentation for 3D Object Detection (CVPR'21) [[paper]](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_PointAugmenting_Cross-Modal_Augmentation_for_3D_Object_Detection_CVPR_2021_paper.html) [[project page]](https://github.com/VISION-SJTU/PointAugmenting)

- FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection  (ITSC'21) [[paper]](https://arxiv.org/pdf/2106.12449) [[project page]](https://github.com/Shaoqing26/FusionPainting)

- Unifying Voxel-based Representation with Transformer for 3D Object Detection (Arxiv'21) [[paper]](https://arxiv.org/pdf/2206.00630) [[project page]](https://github.com/dvlab-research/UVTR)

- TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers (CVPR'22) [[paper]](https://arxiv.org/pdf/2203.11496) [[project page]](https://github.com/XuyangBai/TransFusion)

- AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection (IJCAI'22) [[paper]](https://arxiv.org/pdf/2201.06493) [[project page]](https://github.com/zehuichen123/AutoAlignV2)

- AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection (ECCV'22) [[paper]](https://arxiv.org/pdf/2207.10316v1.pdf) [[project page]](https://github.com/zehuichen123/AutoAlignV2)

- CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection  (WACV'21) [[paper]](https://arxiv.org/pdf]/2011.04841) [[project page]](https://github.com/mrnabati/CenterFusion)

- MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection (Arxiv'22) [[paper]](https://arxiv.org/pdf/2209.03102.pdf)[[project page]](https://github.com/SxJyJay/MSMDFusion)

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![](BEV_Survey/multi-modal-results.png)

Temporal Fusion:

- BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection (Arxiv'22) [[paper]](https://arxiv.org/pdf/2203.17054) [[project page]](https://github.com/HuangJunJie2017/BEVDet)

- Image2Map: Translating Images into Maps (ICRA'22) [[paper]](https://arxiv.org/pdf/2110.00966.pdf) [[project page]](https://github.com/avishkarsaha/translating-images-into-maps)

- FIERY: Future Instance Prediction in Bird’s-Eye View from Surround Monocular Cameras (ICCV'21) [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Hu_FIERY_Future_Instance_Prediction_in_Birds-Eye_View_From_Surround_Monocular_ICCV_2021_paper.pdf) [[project page]](https://github.com/wayveai/fiery)

- Ego3RT: Learning Ego 3D Representation as Ray Tracing (ECCV'22) [[paper]](https://arxiv.org/pdf/2206.04042.pdf) [[project page]](https://github.com/fudan-zvg/Ego3RT)

- PolarFormer: Multi-camera 3D Object Detection with Polar Transformers (Arxiv'22)[[paper]](https://arxiv.org/pdf/2206.15398) [[project page]](https://github.com/fudan-zvg/PolarFormer)

- BEVStitch: Understanding Bird’s-Eye View of Road Semantics using an Onboard Camera (ICRA'22) [[paper]](https://arxiv.org/pdf/2012.03040.pdf) [[project page]](https://github.com/ybarancan/BEV_feat_stitch)

- PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images (Arxiv'22) [[paper]](https://arxiv.org/pdf/2206.01256) [[project page]](https://github.com/megvii-research/PETR)

- BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers (ECCV'22) [[paper]](https://arxiv.org/pdf/2203.17270v1.pdf) [[project page]](https://github.com/zhiqi-li/BEVFormer)

- UniFormer: Unified Multi-view Fusion Transformer for Spatial-Temporal Representation in Bird’s-Eye-View (Arxiv'22) [[paper]](https://arxiv.org/pdf/2207.08536) 

- DfM: Monocular 3D Object Detection with Depth from Motion (ECCV'22) [[paper]](https://arxiv.org/pdf/2207.12988.pdf) [[project page]](https://github.com/Tai-Wang/Depth-from-Motion)

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![](BEV_Survey/temporal-results.png)

Multi-agent Fusion:

- CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers (Arxiv'22)  [[paper]](https://arxiv.org/pdf/2207.02202.pdf)

#### Empirical Know-Hows

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![](BEV_Survey/empirical-know-hows.png)

### Citation

If you find our work useful in your research, please consider citing:

```tex

@inproceedings{Ma2022VisionCentricBP,

  title={Vision-Centric BEV Perception: A Survey},

  author={Yuexin Ma and Tai Wang and Xuyang Bai and Huitong Yang and Yuenan Hou and Yaming Wang and Y. Qiao and Ruigang Yang and Dinesh Manocha and Xinge Zhu},

  year={2022}

}

```

## Contributing

Please feel free to submit a pull request to add the new paper or related project page.

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