EAS-Det: Edge-Aware Semantic Feature Fusion for Robust 3D Object Detection in LiDAR Point Clouds

Accurate 3D object detection and localization in LiDAR point clouds are crucial for applications such as autonomous driving and UAV-based monitoring. However, existing detectors often suffer from the loss of critical geometric information during network processing, mainly due to downsampling and pooling operations. This leads to imprecise object boundaries and degraded detection accuracy, particularly for small objects. To address these challenges, we propose Edge-Aware Semantic Feature Fusion for Detection (EAS-Det), a lightweight, plug-and-play framework for LiDAR-based perception. The core module, Edge-Semantic Interaction (ESI), employs a dual-attention mechanism to adaptively fuse geometric edge cues with high-level semantic context, yielding multi-scale representations that preserve structural details while enhancing contextual awareness. EAS-Det is compatible with mainstream backbones such as PointPillars and PV-RCNN. Extensive experiments on the KITTI and Waymo datasets demonstrate consistent and significant improvements, achieving up to 10.34% and 8.66% AP gains for pedestrians and cyclists, respectively, on the KITTI benchmark. These results underscore the effectiveness and generalizability of EAS-Det for robust 3D object detection in complex real-world environments.