# MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion

arXiv: https://arxiv.org/abs/2009.10945 · HTML: https://mlsanity.com/paper/2009.10945/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection](https://mlsanity.com/paper/2106.12449/) (2021)
- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection](https://mlsanity.com/paper/2206.15157/) (2022)
- [Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection](https://mlsanity.com/paper/2210.09615/) (2022)
- [SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization](https://mlsanity.com/paper/2503.14558/) (2025)
- [Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection](https://mlsanity.com/paper/2212.05265/) (2022)
- [PointSee: Image Enhances Point Cloud](https://mlsanity.com/paper/2211.01664/) (2022)
- [MARS: Multimodal Active Robotic Sensing for Articulated Characterization](https://mlsanity.com/paper/2407.01191/) (2024)
- [CVFNet: Real-time 3D Object Detection by Learning Cross View Features](https://mlsanity.com/paper/2203.06585/) (2022)
- [Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception](https://mlsanity.com/paper/2310.05951/) (2023)

## Abstract (arXiv)

3D vehicle detection based on multi-modal fusion is an important task of many applications such as autonomous driving. Although significant progress has been made, we still observe two aspects that need to be further improvement: First, the specific gain that camera images can bring to 3D detection is seldom explored by previous works. Second, many fusion algorithms run slowly, which is essential for applications with high real-time requirements(autonomous driving). To this end, we propose an end-to-end trainable single-stage multi-modal feature adaptive network in this paper, which uses image information to effectively reduce false positive of 3D detection and has a fast detection speed. A multi-modal adaptive feature fusion module based on channel attention mechanism is proposed to enable the network to adaptively use the feature of each modal. Based on the above mechanism, two fusion technologies are proposed to adapt to different usage scenarios: PointAttentionFusion is suitable for filtering simple false positive and faster; DenseAttentionFusion is suitable for filtering more difficult false positive and has better overall performance. Experimental results on the KITTI dataset demonstrate significant improvement in filtering false positive over the approach using only point cloud data. Furthermore, the proposed method can provide competitive results and has the fastest speed compared to the published state-of-the-art multi-modal methods in the KITTI benchmark.