Efficient point cloud visualization is indispensable for practical applications. In the context of point cloud visualization, 3-D rendering can be viewed as the kernel that transforms 3-D points into a 2-D scene image. Compared with traditional point-based rendering (PBR), neural image-based rendering (NIBR) has gradually emerged as a feasible solution for point cloud rendering. To efficiently render sparse and colorless mobile laser scanning (MLS) point cloud, we propose a novel neural rendering framework based on deep generative learning, named MLS point cloud rendering with generative adversarial network (MPR-GAN). In this framework, perspective projection with intrinsic parameter scaling and cumulative distribution normalization is first utilized to transform the 3-D point cloud into a compact 2-D image; a conditional generative adversarial network (CGAN)-based rendering model is then proposed to generate a photorealistic scene image from the projected 2-D image. In this CGAN model, the asymmetric encoder–decoder generator can implement inpainting and true colorization using context feature capturing and edge information perception; a multiscale discriminator is built to guarantee the model output with global consistency and local details. Moreover, a hybrid loss function is designed to improve the visual quality of the generated images with similarity constraints from both the content and the structure. Two public MLS point cloud datasets are selected and employed to carry out extensive evaluation using MPR-GAN and other baseline frameworks. The experimental results demonstrate that MPR-GAN achieves the state-of-the-art rendering performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Furthermore, the efficiency analysis shows that MPR-GAN can support real-time rendering, achieving end-to-end rendering from raw points.