Nowadays, three-dimensional (3D) point cloud has been an emerging medium to represent real-world scenes and objects. However, there is a considerable proportion of point clouds whose color attribute information is not captured during the acquisition process due to the device or environment limitations. This poses a great challenge for efficient management and utilization of point clouds. To address this problem, we introduce an automatic colorization scheme based on a deep generative network for 3D point clouds. The proposed approach uses the range images of point could geometry and trains a conditional generative adversarial network to predict the color of those images. Later, the color of each pixel in the colorized image is projected back to its corresponding point in the 3D point cloud. The experimental results demonstrate the efficacy of the proposed colorization approach in facilitating users to recognize and handle 3D point cloud data better.