Recent advancements in acquisition of three-dimensional models have been increasingly drawing attention toimaging modalities based on the plenoptic representations, such as light fields and point clouds. Since point cloudmodels can often contain millions of points, each including both geometric positions and associated attributes,efficient compression schemes are needed to enable transmission and storage of this type of media. This project presents a detachable learning-based residual module for point cloud compression that allows for efficientscalable coding.