The ever-growing variety of capture methods and applications for 3D range geometry continually increases the need for efficient methods of data storage and transmission. Compression techniques address this need by offering reduced file sizes while maintaining the precision needed for a particular application. Several such compression methods use phase-shifting principles to encode the 3D data into a 2D RGB image. In some applications, such as telepresence, high precision may only be required in a particular region within a scan. This paper proposes a feature-driven compression method that provides a way to encode regions of interest at higher precision while encoding the remaining data at lower precision to reduce file sizes. This method supports both lossless and lossy compression, enabling even greater file size savings and a wider range of applications. In the case of a depth scan of a bust, an extracted bounding box of the face was used to create an encoding distribution such that the facial region was encoded at higher precisions. When using JPEG 80, the global RMS reconstruction accuracy of this novel encoding was 99.72%; however, in the region of interest, the accuracy was 99.88%. This feature-driven encoding achieved a 26% reduction in compressed file size compared to a fixed, high precision encoding.