Fast-BEV++: Fast by Algorithm, Deployable by Design
Not on the map. Why?
Abstract (arXiv)
The advancement of vision-only BEV (Bird's-Eye-View) perception is hindered by the fundamental trade-off between perception accuracy and deployment efficiency. We introduce Fast-BEV++, resolving this tension through two principles: Fast by Algorithm and Deployable by Design. By decomposing view transformation into a hardware-oriented Index-Gather-Reshape pipeline, Fast-BEV++ eliminates custom kernels while achieving no less than 3 times speedup over baseline methods. Empirically, Fast-BEV++ establishes a new state-of-the-art accuracy-speed trade-off on nuScenes, achieving 0.488 NDS while sustaining real-time inference at over 134 FPS. In particular, depth supervision yields consistent and tangible performance gains, maintaining the highest accuracy among comparable methods. The decomposed architecture enables seamless real-time deployment on production-level platforms, eliminating hardware constraints without loss of efficiency. Code and models are released on the linked project page.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 26 of its 29 references that are in the map. First posted on arXiv 9 Dec 2025.
Builds on
26 of 29 references.
References · 26
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
- 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
- 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
- 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,213
- 2020Center-based 3D Object Detection and Trackingcited by 1,143
- 2022Planning-oriented Autonomous Drivingcited by 1,068
- 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,040
- 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
- 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 844
- 2021BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-Viewcited by 711
14 more
- 2021DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queriescited by 655
- 2022BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detectioncited by 568
- 2022PETR: Position Embedding Transformation for Multi-View 3D Object Detectioncited by 550
- 2021FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detectioncited by 444
- 2022PETRv2: A Unified Framework for 3D Perception from Multi-Camera Imagescited by 351
- 2019Class-balanced Grouping and Sampling for Point Cloud 3D Object Detectioncited by 350
- 2022BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detectioncited by 340
- 2022BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervisioncited by 302
- 2022M2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representationcited by 155
- 2023FB-BEV: BEV Representation from Forward-Backward View Transformationscited by 98
- 2023Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baselinecited by 40
- 2023EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detectioncited by 38
- 2022MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perceptioncited by 29
- 2023Fast-BEV: Towards Real-time On-vehicle Bird's-Eye View Perceptioncited by 18
Built on by
Citing papers per year: 2026: 2.
2 papers.