# MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion

arXiv: https://arxiv.org/abs/2302.10511 · HTML: https://mlsanity.com/paper/2302.10511/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 52 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [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)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://mlsanity.com/paper/2203.11496/) (2022)
- [PointPainting: Sequential Fusion for 3D Object Detection](https://mlsanity.com/paper/1911.10150/) (2019)
- [FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection](https://mlsanity.com/paper/2104.10956/) (2021)
- [Categorical Depth Distribution Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.01100/) (2021)
- [Joint 3D Proposal Generation and Object Detection from View Aggregation](https://mlsanity.com/paper/1712.02294/) (2017)
- [Deep Continuous Fusion for Multi-Sensor 3D Object Detection](https://mlsanity.com/paper/2012.10992/) (2020)
- [Is Pseudo-Lidar needed for Monocular 3D Object detection?](https://mlsanity.com/paper/2108.06417/) (2021)
- [DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2203.08195/) (2022)
- [SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation](https://mlsanity.com/paper/2002.10111/) (2020)
- [CenterMask : Real-Time Anchor-Free Instance Segmentation](https://mlsanity.com/paper/1911.06667/) (2019)
- [Probabilistic and Geometric Depth: Detecting Objects in Perspective](https://mlsanity.com/paper/2107.14160/) (2021)
- [Cross-view Semantic Segmentation for Sensing Surroundings](https://mlsanity.com/paper/1906.03560/) (2019)
- [CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2011.04841/) (2020)
- [RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving](https://mlsanity.com/paper/2001.03343/) (2020)
- [A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection](https://mlsanity.com/paper/2005.07431/) (2020)
- [Ground-aware Monocular 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2102.00690/) (2021)

## Built on by (most cited first, 29 of 29)

- [CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception](https://mlsanity.com/paper/2304.00670/) (2023)
- [RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection](https://mlsanity.com/paper/2403.16440/) (2024)
- [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)
- [Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception](https://mlsanity.com/paper/2403.07746/) (2024)
- [CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation](https://mlsanity.com/paper/2403.19104/) (2024)
- [OmniHD-Scenes: A Next-Generation Multimodal Dataset for Autonomous Driving](https://mlsanity.com/paper/2412.10734/) (2024)
- [Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion](https://mlsanity.com/paper/2307.16532/) (2023)
- [CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection](https://mlsanity.com/paper/2411.03013/) (2024)
- [RCBEVDet++: Toward High-accuracy Radar-Camera Fusion 3D Perception Network](https://mlsanity.com/paper/2409.04979/) (2024)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [RICCARDO: Radar Hit Prediction and Convolution for Camera-Radar 3D Object Detection](https://mlsanity.com/paper/2504.09086/) (2025)
- [SpaRC: Sparse Radar-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2411.19860/) (2024)
- [UniBEVFusion: Unified Radar-Vision BEVFusion for 3D Object Detection](https://mlsanity.com/paper/2409.14751/) (2024)
- [ClusterFusion: Leveraging Radar Spatial Features for Radar-Camera 3D Object Detection in Autonomous Vehicles](https://mlsanity.com/paper/2309.03734/) (2023)
- [CRAB: Camera-Radar Fusion for Reducing Depth Ambiguity in Backward Projection based View Transformation](https://mlsanity.com/paper/2509.05785/) (2025)
- [4DRC-OCC: Robust Semantic Occupancy Prediction Through Fusion of 4D Radar and Camera](https://mlsanity.com/paper/2603.07794/) (2026)
- [A survey on deep learning approaches for data integration in autonomous driving system](https://mlsanity.com/paper/2306.11740/) (2023)
- [A Resource Efficient Fusion Network for Object Detection in Bird's-Eye View using Camera and Raw Radar Data](https://mlsanity.com/paper/2411.13311/) (2024)
- [PAN: Pillars-Attention-Based Network for 3D Object Detection](https://mlsanity.com/paper/2509.15935/) (2025)
- [Weather-Conditioned Branch Routing for Robust LiDAR-Radar 3D Object Detection](https://mlsanity.com/paper/2604.05405/) (2026)
- [Revisiting Radar Camera Alignment by Contrastive Learning for 3D Object Detection](https://mlsanity.com/paper/2504.16368/) (2025)
- [Collaborative Perceiver: Elevating Vision-based 3D Object Detection via Local Density-Aware Spatial Occupancy](https://mlsanity.com/paper/2507.21358/) (2025)
- [RCDINO: Enhancing Radar-Camera 3D Object Detection with DINOv2 Semantic Features](https://mlsanity.com/paper/2508.15353/) (2025)
- [IMKD: Intensity-Aware Multi-Level Knowledge Distillation for Camera-Radar Fusion](https://mlsanity.com/paper/2512.15581/) (2025)
- [CLLAP: Contrastive Learning-based LiDAR-Augmented Pretraining for Enhanced Radar-Camera Fusion](https://mlsanity.com/paper/2604.24044/) (2026)
- [Seeing through boxes: Non-Line-of-Sight 3D Reconstruction from Radar Signals](https://mlsanity.com/paper/2605.29098/) (2026)
- [LLM-Empowered Multimodal Fusion Framework for Autonomous Driving: Semantic Enhancement and Channel-Adaptive Design](https://mlsanity.com/paper/2607.01772/) (2026)
- [CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining](https://mlsanity.com/paper/2607.04541/) (2026)

## Abstract (arXiv)

Multi-view radar-camera fused 3D object detection provides a farther detection range and more helpful features for autonomous driving, especially under adverse weather. The current radar-camera fusion methods deliver kinds of designs to fuse radar information with camera data. However, these fusion approaches usually adopt the straightforward concatenation operation between multi-modal features, which ignores the semantic alignment with radar features and sufficient correlations across modals. In this paper, we present MVFusion, a novel Multi-View radar-camera Fusion method to achieve semantic-aligned radar features and enhance the cross-modal information interaction. To achieve so, we inject the semantic alignment into the radar features via the semantic-aligned radar encoder (SARE) to produce image-guided radar features. Then, we propose the radar-guided fusion transformer (RGFT) to fuse our radar and image features to strengthen the two modals' correlation from the global scope via the cross-attention mechanism. Extensive experiments show that MVFusion achieves state-of-the-art performance (51.7% NDS and 45.3% mAP) on the nuScenes dataset. We shall release our code and trained networks upon publication.