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Meta AI Research has recently released SAM (Segment Anything Model) which istrained on a large segmentation dataset of over 1 billion masks. As afoundation model in the field of computer vision, SAM (Segment Anything Model)has gained attention for its impressive performance in generic objectsegmentation. Despite its strong capability in a wide range of zero-shottransfer tasks, it remains unknown whether SAM can detect things in challengingsetups like transparent objects. In this work, we perform an empiricalevaluation of two glass-related challenging scenarios: mirror and transparentobjects. We found that SAM often fails to detect the glass in both scenarios,which raises concern for deploying the SAM in safety-critical situations thathave various forms of glass.

Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected