Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection

• URL: http://arxiv.org/abs/2502.01856v1
• Date: Mon, 03 Feb 2025 22:07:14 GMT
• Title: Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection
• Authors: Reza Sadeghian, Niloofar Hooshyaripour, Chris Joslin, WonSook Lee,
• Abstract summary: We propose ReliFusion, a LiDAR-camera fusion framework operating in the bird's-eye view (BEV) space. ReliFusion integrates three key components: the Spatio-Temporal Feature Aggregation (STFA) module, the Reliability module, and the Confidence-Weighted Mutual Cross-Attention (CW-MCA) module. Experiments on the nuScenes dataset show that ReliFusion significantly outperforms state-of-the-art methods, achieving superior robustness and accuracy in scenarios with limited LiDAR fields of view and severe sensor malfunctions.
• Score: 0.0
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• Abstract: Accurate and robust 3D object detection is essential for autonomous driving, where fusing data from sensors like LiDAR and camera enhances detection accuracy. However, sensor malfunctions such as corruption or disconnection can degrade performance, and existing fusion models often struggle to maintain reliability when one modality fails. To address this, we propose ReliFusion, a novel LiDAR-camera fusion framework operating in the bird's-eye view (BEV) space. ReliFusion integrates three key components: the Spatio-Temporal Feature Aggregation (STFA) module, which captures dependencies across frames to stabilize predictions over time; the Reliability module, which assigns confidence scores to quantify the dependability of each modality under challenging conditions; and the Confidence-Weighted Mutual Cross-Attention (CW-MCA) module, which dynamically balances information from LiDAR and camera modalities based on these confidence scores. Experiments on the nuScenes dataset show that ReliFusion significantly outperforms state-of-the-art methods, achieving superior robustness and accuracy in scenarios with limited LiDAR fields of view and severe sensor malfunctions.

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