The enhancement of 3D object detection is pivotal for precise environmental perception and improved task execution capabilities in autonomous driving. LiDAR point clouds, offering accurate depth information, serve as a crucial information for this purpose. Our study focuses on key challenges in 3D target detection. To tackle the challenge of expanding the receptive field of a 3D convolutional kernel, we introduce the Dynamic Feature Fusion Module (DFFM). This module achieves adaptive expansion of the 3D convolutional kernel's receptive field, balancing the expansion with acceptable computational loads. This innovation reduces operations, expands the receptive field, and allows the model to dynamically adjust to different object requirements. Simultaneously, we identify redundant information in 3D features. Employing the Feature Selection Module (FSM) quantitatively evaluates and eliminates non-important features, achieving the separation of output box fitting and feature extraction. This innovation enables the detector to focus on critical features, resulting in model compression, reduced computational burden, and minimized candidate frame interference. Extensive experiments confirm that both DFFM and FSM not only enhance current benchmarks, particularly in small target detection, but also accelerate network performance. Importantly, these modules exhibit effective complementarity.

翻译：三维目标检测的增强对于自动驾驶中精确的环境感知和提升任务执行能力至关重要。提供精确深度信息的LiDAR点云是实现这一目标的关键数据。本研究聚焦于三维目标检测的核心挑战。针对三维卷积核感受野扩展的难题，我们引入了动态特征融合模块（DFFM）。该模块实现了三维卷积核感受野的自适应扩展，在扩展感受野的同时保持了可接受的计算负载。这一创新减少了运算量，扩大了感受野，并使得模型能够动态适应不同目标的需求。同时，我们识别出三维特征中的冗余信息。通过采用特征选择模块（FSM）对非重要特征进行定量评估与剔除，实现了输出框拟合与特征提取的分离。这一创新使检测器能够聚焦于关键特征，从而实现模型压缩、降低计算负担并减少候选框干扰。大量实验证实，DFFM与FSM不仅能提升现有基准性能（尤其在小型目标检测方面），还能加速网络运行。重要的是，这两个模块展现出有效的互补性。