The growing demand for sweetpotato French fry and other processed products has increased the need for producing storage roots of desired shape profile (i.e., blocky and less tapered). Length-width ratio (LW) is the current de facto standard for characterizing sweetpotato shape. Although LW is sensitive and descriptive of some types of shape variability, this index may be inadequate to measure taper and other subtle shape variations. Prior work has shown that surface area (SA) and volume (VOL) are important shape descriptors but current direct measurement methods are tedious, inconsistent, and often destructive. A low-cost three-dimensional (3D) scanner was used to acquire digital 3D models of 210 U.S. No. 1 grade sweetpotato storage roots. The 3D models were imported into Meshmixer, a free software for cleaning and processing 3D files. Processing steps included gap filling and rendering the models water-tight to facilitate VOL measurements. The software includes a tool that enables automatic measurements of length (L), width (W), SA, and VOL. LW and SA-VOL ratio (SAVOL) were subsequently calculated. Separately, a digital caliper was used for manual measurements of L and W. The shrink-wrap method was used to measure SA, and water displacement was used to measure VOL. 3D scanner-based and manual L measurements showed high correlation, whereas VOL was lowest. Principal component analysis (PCA) of 3D scanner-based measurements showed that the first two principal components (PCs) accounted for 96.2% of the total shape variation in the data set, named Ib3D. The first PC accounted for 62.15% of the total variance, and captured variation in storage root shape through changes in VOL, SA, SAVOL, and W. The second PC accounted for 34.4% of the variance, and the main factors were LW and L. Most storage root samples that were classified as processing types were located in the fourth quadrant. The methods described in this work to nondestructively acquire 3D models of sweetpotato also can be adopted for analyzing shape in other horticultural produce like fruits, vegetables, tubers, and other storage roots that meet the specifications for 3D scanning. The data support the hypothesis that knowledge of variables that determine storage root L and W can lead to the development of methods and approaches for enhanced processing product recovery and size assortment for fresh market.