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

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arxiv 2305.00278 v1 pith:GZ65QUCK submitted 2023-04-29 cs.CV cs.AIcs.LG

Dongsheng Han , Chaoning Zhang , Yu Qiao , Maryam Qamar , Yuna Jung , SeungKyu Lee , Sung-Ho Bae , Choong Seon Hong This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords modelanythingglassobjectssegmenttransparentchallengingdetect
verification ladder T0 review T1 audit T2 compute T3 formal
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Meta AI Research has recently released SAM (Segment Anything Model) which is trained on a large segmentation dataset of over 1 billion masks. As a foundation model in the field of computer vision, SAM (Segment Anything Model) has gained attention for its impressive performance in generic object segmentation. Despite its strong capability in a wide range of zero-shot transfer tasks, it remains unknown whether SAM can detect things in challenging setups like transparent objects. In this work, we perform an empirical evaluation of two glass-related challenging scenarios: mirror and transparent objects. We found that SAM often fails to detect the glass in both scenarios, which raises concern for deploying the SAM in safety-critical situations that have various forms of glass.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. OGG-FR: Orthogonal Gradient Gaming and Frequency Rectification for Unmanned Aerial Vehicle Infrared Image Super-Resolution

    cs.CV 2026-08 conditional novelty 6.0 of 10

    OGG-FR is a plug-and-play training update that separates redundant and innovative parts of the FFT loss gradient and gates the innovative part by a confidence score, improving UAV infrared super-resolution in most tes...

  2. SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A prompting scheme that mines points, elastic boxes, and Gaussian-style masks from coarse masks lets SAM refine those masks more accurately than prior refinement tools.

  3. Efficient Track Anything

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A lightweight video segmentation model with a vanilla ViT encoder and pooled memory cross-attention matches SAM 2 closely while running twice as fast and using 2.4x fewer parameters.

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