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  11. Bi-att3ddet: Attention-based Bi-directional Fusion For Multi-modal 3d Object Detection

Bi-Att3DDet: Attention-Based Bi-Directional Fusion for Multi-Modal 3D Object Detection

Xu Gao1,2, Yaqian Zhao1,2, Yanan Wang1,2

  • 1School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China.

Sensors (Basel, Switzerland)
|February 13, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces Bi-Att3DDet, a novel network for multi-modal 3D object detection in autonomous driving. It enhances feature fusion between LiDAR and RGB data, improving detection accuracy and utilizing complementary sensor information effectively.

Area of Science:

  • Computer Vision
  • Autonomous Driving Systems
  • Sensor Fusion

Background:

  • Multi-modal 3D object detection is crucial for autonomous driving.
  • Existing methods struggle with effective fusion of LiDAR and RGB image features.
  • Inadequate capture of structural information in Region of Interest (RoI) features limits performance.

Purpose of the Study:

  • To propose Bi-Att3DDet, a multi-modal sensor fusion network for improved 3D object detection.
  • To enhance the utilization of complementary information between depth and semantic texture features.
  • To better capture structural information within RoI features for more accurate detections.

Main Methods:

  • Developed a Self-Attentive RoI Feature Extraction module (SARoIFE) using self-attention mechanisms.
Keywords:
3D object detectionattention mechanismautonomous drivingmulti-modal sensor fusion

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  • Implemented a Feature Bidirectional Interactive Fusion module (FBIF) for LiDAR and pseudo RoI features.
  • Employed comprehensive experiments on the KITTI dataset to validate the proposed method.

    • Main Results:

    • Achieved a 1.55% improvement on the hard difficulty level.
    • Secured a 0.19% improvement in mean Average Precision (mAP) on the test dataset.
    • Demonstrated effective fusion of complementary sensor information and improved RoI feature extraction.

    Conclusions:

    • Bi-Att3DDet significantly enhances multi-modal 3D object detection performance.
    • The proposed SARoIFE and FBIF modules effectively address limitations in feature fusion and structural information capture.
    • The method shows strong potential for real-world autonomous driving applications.