VoxelNet is the first end-to-end object detection model based on point cloud. Taken only point cloud data as input, it has good effect. However, in VoxelNet, taking point cloud data of full scene as input makes more computation resources use on background point cloud data, and error detection and missing detection are easy to occur in complex scenes because the point cloud with only geometrical information has low recognition granularity on the targets. In order to solve these problems, an improved VoxelNet model with view frustum added was proposed. Firstly, the targets of interest were located by the RGB front view image. Then, the dimension increase was performed on the 2D targets, making the targets into the spatial view frustum. And the view frustum candidate region was extracted in the point cloud to filter out the redundant point cloud, only the point cloud within view frustum candidate region was calculated to obtain the detection results. Compared with VoxelNet, the improved algorithm reduces computation complexity of point cloud, and avoids the calculation of background point cloud data, so as to increase the efficiency of detection. At the same time, it avoids the disturbance of redundant background points and decreases the error detection rate and missing detection rate. The experimental results on KITTI dataset show that the improved algorithm outperforms VoxelNet in 3D detection with 67.92%, 59.98%, 53.95% average precision at easy, moderate and hard level.