Inferring cross-sections of 3D objects: a 3D spatial ability test instrument for 3D volume segmentation

Abstract

Understanding 3D shapes through cross-sections is a mental task that appears both in 3D volume segmentation and solid modeling tasks. Similar to other shape understanding tasks --- such as paper folding --- performance on this task varies across the population, and can be improved through training and practice. We are --- long term --- interested in creating training tools for 3D volume segmentation. To this end, we have modified (and evaluated) an existing cross-section performance measure in the context of our intended application. Our primary adaptations were 1) to use 3D stimuli (instead of 2D) to more accurately capture the real-world application and 2) evaluate performance on 3D biological shapes relative to the 3D geometric shapes used in the previous study. Our findings are: 1) Participants had the same pattern of errors as the original study, but overall their performance improved when they could see the objects rotating in 3D. 2) Inferring cross-sections of biological shapes is more challenging than pure geometric shapes.