  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.
