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

Understand

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.