Point cloud (PC) compression is crucial to immersive visual applications such as autonomous vehicles to classify objects on the roads. The MPEG standardization group has achieved a notable compression efficiency, called video-based point-cloud compression (V-PCC), which consists of an encoder-decoder. The V-PCC encoder takes original 3D PC data and projects them onto multiple 2D planes to generate several 2D feature images. These images are then compressed using the well-established High-Efficiency Video Coding (HEVC) method. The V-PCC decoder uses compressed information and decoding techniques to reconstruct the 3D point cloud. However, the point clouds produced by V-PCC are often sparse, non-uniform, and contain artifacts. In many practical applications, it is necessary to recover complete point clouds from partial ones in real time. This paper presents a method for enhancing decoded point clouds as a post-processing step in the V-PCC with reduced computational time. Our approach involves a 2D upsampling for the V-PCC occupancy image, which increases the density of the point cloud, and a 2D high-resolution auxiliary information modification algorithm for the 2D-3D conversion of high-resolution 3D point clouds, which improves the uniformity and reduces the noise in the point cloud. The 3D high-resolution point cloud has been further enhanced using the developed 3D outlier removal and point regeneration algorithm. Our proposed work can significantly simplify the state-of-the-art superresolution methods for point clouds and reduce the time complexity of \(61\%\sim 75\%,\) while maintaining a high level of quality in point clouds.