Three-dimensional light detection and ranging (3D LiDAR) sensors are widely used in autonomous vehicles. Among various perception problems using a 3D LiDAR, the identification of drivable region (DR) is one of the most important problems. In this paper, the DR identification using a 3D LiDAR is reformulated as a completion problem. In other words, the problem is to generate the virtual points so that the virtual points cover the DR as densely and uniformly as possible, thereby representing the DR well enough. The problem considered herein is named the DR completion problem. To solve the DRC, a new network called the Drivable Region Completion Network (DRCN) is proposed. The proposed DRCN consists of an encoder part and a decoder part. The encoder part consists of two encoders. The first one is the Multi-Layer Perceptron (MLP) encoder, and it captures the global feature of the given LiDAR points. The second one is named the Point Pyramid Network (PPN) encoder, and it extracts local features between the points in the point cloud. The decoder part takes the coarse-to-fine structure, and it consists of the coarse and refinement decoders. The coarse decoder predicts seed points for the DR. The refinement decoder refines the seed points to predict the final dense DR points. Finally, the DRCN is applied to the SemanticKITTI dataset for evaluation. The proposed DRCN is validated by showing that our DRCN outperforms other DR detection methods in terms of accuracy.