# BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection

arXiv: https://arxiv.org/abs/2312.01696 · HTML: https://mlsanity.com/paper/2312.01696/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 63 of its 70 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 30 of 63)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [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)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://mlsanity.com/paper/2203.11496/) (2022)
- [FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection](https://mlsanity.com/paper/2104.10956/) (2021)
- [PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images](https://mlsanity.com/paper/2206.01256/) (2022)
- [Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection](https://mlsanity.com/paper/1908.09492/) (2019)
- [BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection](https://mlsanity.com/paper/2203.17054/) (2022)
- [Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving](https://mlsanity.com/paper/2304.14365/) (2023)
- [Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection](https://mlsanity.com/paper/2303.11926/) (2023)
- [BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision](https://mlsanity.com/paper/2211.10439/) (2022)

## Built on by (most cited first, 30 of 35)

- [Perspective-Invariant 3D Object Detection](https://mlsanity.com/paper/2507.17665/) (2025)
- [UAV3D: A Large-scale 3D Perception Benchmark for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2410.11125/) (2024)
- [AD-H: Language-guided Autonomous Driving with Hierarchical Agents](https://mlsanity.com/paper/2406.03474/) (2024)
- [SimPB: A Single Model for 2D and 3D Object Detection from Multiple Cameras](https://mlsanity.com/paper/2403.10353/) (2024)
- [Make Your ViT-based Multi-view 3D Detectors Faster via Token Compression](https://mlsanity.com/paper/2409.00633/) (2024)
- [Bridging Perspectives: Foundation Model Guided BEV Maps for 3D Object Detection and Tracking](https://mlsanity.com/paper/2510.10287/) (2025)
- [DriveMamba: Task-Centric Scalable State Space Model for Efficient End-to-End Autonomous Driving](https://mlsanity.com/paper/2602.13301/) (2026)
- [SparseFusion: Efficient Sparse Multi-Modal Fusion Framework for Long-Range 3D Perception](https://mlsanity.com/paper/2403.10036/) (2024)
- [SpaRC: Sparse Radar-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2411.19860/) (2024)
- [Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels](https://mlsanity.com/paper/2503.08421/) (2025)
- [LightOcc: Lightweight Spatial Embedding for Efficient Vision-based 3D Occupancy Prediction](https://mlsanity.com/paper/2412.05976/) (2024)
- [CHARM3R: Towards Unseen Camera Height Robust Monocular 3D Detector](https://mlsanity.com/paper/2508.11185/) (2025)
- [Learning High-resolution Vector Representation from Multi-Camera Images for 3D Object Detection](https://mlsanity.com/paper/2407.15354/) (2024)
- [MIC-BEV: Multi-Infrastructure Camera Bird's-Eye-View Transformer with Relation-Aware Fusion for 3D Object Detection](https://mlsanity.com/paper/2510.24688/) (2025)
- [Towards Intrinsic-Aware Monocular 3D Object Detection](https://mlsanity.com/paper/2603.27059/) (2026)
- [HV-BEV: Decoupling Horizontal and Vertical Feature Sampling for Multi-View 3D Object Detection](https://mlsanity.com/paper/2412.18884/) (2024)
- [HeightFormer: Learning Height Prediction in Voxel Features for Roadside Vision Centric 3D Object Detection via Transformer](https://mlsanity.com/paper/2503.10777/) (2025)
- [DSERT-RoLL: Robust Multi-Modal Perception for Diverse Driving Conditions with Stereo Event-RGB-Thermal Cameras, 4D Radar, and Dual-LiDAR](https://mlsanity.com/paper/2604.03685/) (2026)
- [DenseBEV: Transforming BEV Grid Cells into 3D Objects](https://mlsanity.com/paper/2512.16818/) (2025)
- [Leveraging Previous-Traversal Point Cloud Map Priors for Camera-Based 3D Object Detection and Tracking](https://mlsanity.com/paper/2604.25405/) (2026)
- [SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset](https://mlsanity.com/paper/2502.01894/) (2025)
- [SABER: Spatially Consistent 3D Universal Adversarial Objects for BEV Detectors](https://mlsanity.com/paper/2505.22499/) (2025)
- [OcRFDet: Object-Centric Radiance Fields for Multi-View 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2506.23565/) (2025)
- [Neural Rendering for Sensor Adaptation in 3D Object Detection](https://mlsanity.com/paper/2508.12695/) (2025)
- [MASAR: Motion-Appearance Synergy Refinement for Joint Detection and Trajectory Forecasting](https://mlsanity.com/paper/2602.13003/) (2026)
- [SemLT3D: Semantic-Guided Expert Distillation for Camera-only Long-Tailed 3D Object Detection](https://mlsanity.com/paper/2604.18476/) (2026)
- [Revisiting Radar Camera Alignment by Contrastive Learning for 3D Object Detection](https://mlsanity.com/paper/2504.16368/) (2025)
- [Automating Infrastructure Surveying: A Framework for Geometric Measurements and Compliance Assessment Using Point Cloud Data](https://mlsanity.com/paper/2505.05752/) (2025)
- [DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos](https://mlsanity.com/paper/2506.10242/) (2025)
- [SDCM: Simulated Densifying and Compensatory Modeling Fusion for Radar-Vision 3-D Object Detection in Internet of Vehicles](https://mlsanity.com/paper/2602.00149/) (2026)

## Abstract (arXiv)

Recently, the rise of query-based Transformer decoders is reshaping camera-based 3D object detection. These query-based decoders are surpassing the traditional dense BEV (Bird's Eye View)-based methods. However, we argue that dense BEV frameworks remain important due to their outstanding abilities in depth estimation and object localization, depicting 3D scenes accurately and comprehensively. This paper aims to address the drawbacks of the existing dense BEV-based 3D object detectors by introducing our proposed enhanced components, including a CRF-modulated depth estimation module enforcing object-level consistencies, a long-term temporal aggregation module with extended receptive fields, and a two-stage object decoder combining perspective techniques with CRF-modulated depth embedding. These enhancements lead to a "modernized" dense BEV framework dubbed BEVNeXt. On the nuScenes benchmark, BEVNeXt outperforms both BEV-based and query-based frameworks under various settings, achieving a state-of-the-art result of 64.2 NDS on the nuScenes test set. Code will be available at urlhttps://github.com/woxihuanjiangguo/BEVNeXt.