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

arXiv: https://arxiv.org/abs/2502.01856 · HTML: https://mlsanity.com/paper/2502.01856/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [RESBev: Making BEV Perception More Robust](https://mlsanity.com/paper/2603.09529/) (2026)
- [CrossRay3D: Geometry and Distribution Guidance for Efficient Multimodal 3D Detection](https://mlsanity.com/paper/2510.15991/) (2025)
- [SARFusion: Scene-Aware Routing Fusion for Robust Camera-LiDAR 3D Object Detection](https://mlsanity.com/paper/2609.29235/) (2026)

## Abstract (arXiv)

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.