  In our increasingly diverse society, everyday physical interfaces often
present barriers, impacting individuals across various contexts. This
oversight, from small cabinet knobs to identical wall switches that can pose
different contextual challenges, highlights an imperative need for solutions.
Leveraging low-cost 3D-printed augmentations such as knob magnifiers and
tactile labels seems promising, yet the process of discovering unrecognized
barriers remains challenging because disability is context-dependent. We
introduce AccessLens, an end-to-end system designed to identify inaccessible
interfaces in daily objects, and recommend 3D-printable augmentations for
accessibility enhancement. Our approach involves training a detector using the
novel AccessDB dataset designed to automatically recognize 21 distinct
Inaccessibility Classes (e.g., bar-small and round-rotate) within 6 common
object categories (e.g., handle and knob). AccessMeta serves as a robust way to
build a comprehensive dictionary linking these accessibility classes to
open-source 3D augmentation designs. Experiments demonstrate our detector's
performance in detecting inaccessible objects.
