UniBEVFusion: Unified Radar-Vision BEVFusion for 3D Object Detection
Haocheng Zhao, Runwei Guan, Taoyu Wu, Ka Lok Man, Limin Yu, Yutao Yue

TL;DR
UniBEVFusion introduces a novel radar-vision fusion model that enhances 3D object detection by integrating radar-specific data and shared feature extraction, demonstrating superior robustness and accuracy on multiple datasets.
Contribution
The paper presents the RDL module for radar data integration and the UFF approach for unified feature fusion, improving robustness and performance in radar-vision 3D detection.
Findings
Outperforms state-of-the-art models on TJ4D dataset
Enhances robustness against vision modality failure
Achieves significant accuracy improvements in 3D detection
Abstract
4D millimeter-wave (MMW) radar, which provides both height information and dense point cloud data over 3D MMW radar, has become increasingly popular in 3D object detection. In recent years, radar-vision fusion models have demonstrated performance close to that of LiDAR-based models, offering advantages in terms of lower hardware costs and better resilience in extreme conditions. However, many radar-vision fusion models treat radar as a sparse LiDAR, underutilizing radar-specific information. Additionally, these multi-modal networks are often sensitive to the failure of a single modality, particularly vision. To address these challenges, we propose the Radar Depth Lift-Splat-Shoot (RDL) module, which integrates radar-specific data into the depth prediction process, enhancing the quality of visual Bird-Eye View (BEV) features. We further introduce a Unified Feature Fusion (UFF) approach…
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Taxonomy
TopicsAdvanced SAR Imaging Techniques · Infrared Target Detection Methodologies · Optical Systems and Laser Technology
