  3D object detection plays a crucial role in environmental perception for
autonomous vehicles, which is the prerequisite of decision and control. This
paper analyses partition-based methods' inherent drawbacks. In the partition
operation, a single instance such as a pedestrian is sliced into several
pieces, which we call it the partition effect. We propose the Spatial-Attention
Graph Convolution (S-AT GCN), forming the Feature Enhancement (FE) layers to
overcome this drawback. The S-AT GCN utilizes the graph convolution and the
spatial attention mechanism to extract local geometrical structure features.
This allows the network to have more meaningful features for the foreground.
Our experiments on the KITTI 3D object and bird's eye view detection show that
S-AT Conv and FE layers are effective, especially for small objects. FE layers
boost the pedestrian class performance by 3.62\% and cyclist class by 4.21\% 3D
mAP. The time cost of these extra FE layers are limited. PointPillars with FE
layers can achieve 48 PFS, satisfying the real-time requirement.
