# MV2DFusion: Leveraging Modality-Specific Object Semantics for Multi-Modal 3D Detection

arXiv: https://arxiv.org/abs/2408.05945 · HTML: https://mlsanity.com/paper/2408.05945/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 70 of its 79 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 30 of 70)

- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (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)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (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)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [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)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [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)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://mlsanity.com/paper/1711.08488/) (2017)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)

## Built on by (most cited first, 23 of 23)

- [Distilling Multi-modal Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2501.09757/) (2025)
- [Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision](https://mlsanity.com/paper/2504.02477/) (2025)
- [Bridging Perspectives: Foundation Model Guided BEV Maps for 3D Object Detection and Tracking](https://mlsanity.com/paper/2510.10287/) (2025)
- [RoCA: Robust Cross-Domain End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.10145/) (2025)
- [Timealign: A multi-modal object detection method for time misalignment fusing in autonomous driving](https://mlsanity.com/paper/2412.10033/) (2024)
- [Rethinking the Spatio-Temporal Alignment of End-to-End 3D Perception](https://mlsanity.com/paper/2512.23635/) (2025)
- [CCF: Complementary Collaborative Fusion for Domain Generalized Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2603.23276/) (2026)
- [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)
- [EVT: Efficient View Transformation for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2411.10715/) (2024)
- [DGFusion: Dual-guided Fusion for Robust Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2511.10035/) (2025)
- [Offline-Poly: A Polyhedral Framework For Offline 3D Multi-Object Tracking](https://mlsanity.com/paper/2602.13772/) (2026)
- [FOMO-3D: Using Vision Foundation Models for Long-Tailed 3D Object Detection](https://mlsanity.com/paper/2603.08611/) (2026)
- [MambaFusion: Adaptive State-Space Fusion for Multimodal 3D Object Detection](https://mlsanity.com/paper/2602.08126/) (2026)
- [Fusion-Poly: A Polyhedral Framework Based on Spatial-Temporal Fusion for 3D Multi-Object Tracking](https://mlsanity.com/paper/2603.08199/) (2026)
- [An Efficient LiDAR-Camera Fusion Network for Multi-Class 3D Dynamic Object Detection and Trajectory Prediction](https://mlsanity.com/paper/2504.13647/) (2025)
- [SCAFusion: A Multimodal 3D Detection Framework for Small Object Detection in Lunar Surface Exploration](https://mlsanity.com/paper/2512.22503/) (2025)
- [Fusion4CA: Boosting 3D Object Detection via Comprehensive Image Exploitation](https://mlsanity.com/paper/2603.05305/) (2026)
- [RayMamba: Ray-Aligned Serialization for Long-Range 3D Object Detection](https://mlsanity.com/paper/2604.02903/) (2026)
- [Control Your Queries: Heterogeneous Query Interaction for Camera-Radar Fusion](https://mlsanity.com/paper/2604.25574/) (2026)
- [Hyperbolic Distillation: Geometry-Guided Cross-Modal Transfer for Robust 3D Object Detection](https://mlsanity.com/paper/2605.09899/) (2026)
- [GraphBEV++: Multi-Modal Feature Alignment for Autonomous Driving](https://mlsanity.com/paper/2606.16354/) (2026)
- [GaussianFusion: Unified 3D Gaussian Representation for Multi-Modal Fusion Perception](https://mlsanity.com/paper/2607.00746/) (2026)
- [MatchFusion: Explicit-Implicit Instance Matching for Spatio-Temporal Multimodal Autonomous Driving](https://mlsanity.com/paper/2609.25860/) (2026)

## Abstract (arXiv)

The rise of autonomous vehicles has significantly increased the demand for robust 3D object detection systems. While cameras and LiDAR sensors each offer unique advantages--cameras provide rich texture information and LiDAR offers precise 3D spatial data--relying on a single modality often leads to performance limitations. This paper introduces MV2DFusion, a multi-modal detection framework that integrates the strengths of both worlds through an advanced query-based fusion mechanism. By introducing an image query generator to align with image-specific attributes and a point cloud query generator, MV2DFusion effectively combines modality-specific object semantics without biasing toward one single modality. Then the sparse fusion process can be accomplished based on the valuable object semantics, ensuring efficient and accurate object detection across various scenarios. Our framework's flexibility allows it to integrate with any image and point cloud-based detectors, showcasing its adaptability and potential for future advancements. Extensive evaluations on the nuScenes and Argoverse2 datasets demonstrate that MV2DFusion achieves state-of-the-art performance, particularly excelling in long-range detection scenarios.