# Fusion4CA: Boosting 3D Object Detection via Comprehensive Image Exploitation Source: [Fusion4CA: Boosting 3D Object Detection via Comprehensive Image Exploitation]() Collection index: [llms.txt]() Data updated: 2026-03-07T05:00:10.880Z Authors: Kang Luo, Xin Chen, Yangyi Xiao, Hesheng Wang Published: 2026-03-05T15:46:14.000Z ## Abstract Nowadays, an increasing number of works fuse LiDAR and RGB data in the bird's-eye view (BEV) space for 3D object detection in autonomous driving systems. However, existing methods suffer from over-reliance on the LiDAR branch, with insufficient exploration of RGB information. To tackle this issue, we propose Fusion4CA, which is built upon the classic BEVFusion framework and dedicated to fully exploiting visual input with plug-and-play components. Specifically, a contrastive alignment module is designed to calibrate image features with 3D geometry, and a camera auxiliary branch is introduced to mine RGB information sufficiently during training. For further performance enhancement, we leverage an off-the-shelf cognitive adapter to make the most of pretrained image weights, and integrate a standard coordinate attention module into the fusion stage as a supplementary boost. Experiments on the nuScenes dataset demonstrate that our method achieves 69.7% mAP with only 6 training epochs and a mere 3.48% increase in inference parameters, yielding a 1.2% improvement over the baseline which is fully trained for 20 epochs. Extensive experiments in a simulated lunar environment further validate the effectiveness and generalization of our method. Our code will be released through Fusion4CA. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Object Detection on Simulated Lunar Dataset](): 3D Object Detection; Simulated Lunar Dataset; mAP: 90.9. - [3D Object Detection on nuScenes (val)](): 3D Object Detection; nuScenes (val); NDS: 72.1. - [3D Object Detection on nuScenes (test)](): 3D Object Detection; nuScenes (test); mAP: 69.7. ## Original sources and implementation [Original paper]() [PDF]()