3D object detection is one of the most popular research fields in computer vision. In the self-driving system, the 3D object detection technology detects the surrounding objects by capturing the surrounding point cloud information and RGB image information, thereby planning the upcoming route for the vehicle. Therefore, it is of great importance to attain the accurate detection and perception of the surrounding environment. To address the loss of foreground points incurred by random sampling in the field of 3D object detection, a random sampling algorithm based on semantic segmentation was proposed, which guided the sampling process through the predicted semantic features, so as to increase the sampling proportion of foreground points and heighten the precision of 3D object detection. Secondly, to address the inconsistency between the location confidence of 3D object detection and the classification confidence, the CL joint loss was proposed, leading the network to select the 3D bounding box with high location confidence and classification confidence, so as to prevent the ambiguity caused by the traditional NMS only considering the classification confidence. Experiments on KITTI 3D object detection datasets show that the proposed method can improve the precision at the three levels of difficulties: easy, moderate, and hard, which verifies the effectiveness of the method in 3D object detection task.