  The development of AR and VR technologies is enhancing users' online shopping
experiences in various ways. However, in existing VR shopping applications,
shopping contexts merely refer to the products and virtual malls or
metaphorical scenes where users select products. This leads to the defect that
users can only imagine rather than intuitively feel whether the selected
products are suitable for their real usage scenes, resulting in a significant
discrepancy between their expectations before and after the purchase. To
address this issue, we propose PaRUS, a VR shopping approach that focuses on
the context between products and their real usage scenes. PaRUS begins by
rebuilding the virtual scenario of the products' real usage scene through a new
semantic scene reconstruction pipeline, which preserves both the structured
scene and textured object models in the scene. Afterwards, intuitive
visualization of how the selected products fit the reconstructed virtual scene
is provided. We conducted two user studies to evaluate how PaRUS impacts user
experience, behavior, and satisfaction with their purchase. The results
indicated that PaRUS significantly reduced the perceived performance risk and
improved users' trust and satisfaction with their purchase results.
