# Cross-Domain Spatial Matching for Camera and Radar Sensor Data Fusion in Autonomous Vehicle Perception System

arXiv: https://arxiv.org/abs/2404.16548 · HTML: https://mlsanity.com/paper/2404.16548/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 39 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 28 of 28)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [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)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [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)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [Joint 3D Proposal Generation and Object Detection from View Aggregation](https://mlsanity.com/paper/1712.02294/) (2017)
- [FUTR3D: A Unified Sensor Fusion Framework for 3D Detection](https://mlsanity.com/paper/2203.10642/) (2022)
- [PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation](https://mlsanity.com/paper/1711.10871/) (2017)
- [CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2011.04841/) (2020)
- [A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection](https://mlsanity.com/paper/2005.07431/) (2020)
- [Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation](https://mlsanity.com/paper/1904.11466/) (2019)
- [LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1809.07941/) (2018)
- [MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review](https://mlsanity.com/paper/2108.03004/) (2021)
- [NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving](https://mlsanity.com/paper/2209.14499/) (2022)

## Abstract (arXiv)

In this paper, we propose a novel approach to address the problem of camera and radar sensor fusion for 3D object detection in autonomous vehicle perception systems. Our approach builds on recent advances in deep learning and leverages the strengths of both sensors to improve object detection performance. Precisely, we extract 2D features from camera images using a state-of-the-art deep learning architecture and then apply a novel Cross-Domain Spatial Matching (CDSM) transformation method to convert these features into 3D space. We then fuse them with extracted radar data using a complementary fusion strategy to produce a final 3D object representation. To demonstrate the effectiveness of our approach, we evaluate it on the NuScenes dataset. We compare our approach to both single-sensor performance and current state-of-the-art fusion methods. Our results show that the proposed approach achieves superior performance over single-sensor solutions and could directly compete with other top-level fusion methods.