ReliefSAM: A Geometry-Augmented Multi-Prior Adapter for Bas-Relief Segmentation

Bas-relief sculptures are vital historical records. However,many are inaccessible for modern 3D scanning due to damage or occlu-sion, leaving archival monocular photographs as the only available visualdata. Semantic segmentation, such as isolating human figures in theseimages, is essential for digital documentation and subsequent archaeo-logical analysis, yet remains highly challenging for foundation modelslike the Segment Anything Model (SAM). The intrinsic properties ofbas-reliefs, including extreme material homogeneity, subtle depth varia-tions, and indistinct soft edges, result in a severe lack of both chromaticand geometric contrast. Consequently, models pretrained on natural im-agery struggle to perceive the shallow 2.5D structural cues embeddedin relief surfaces, leading to imprecise boundary delineation. To addressthis limitation, we propose ReliefSAM, a prompt-free and parameter-efficient framework that explicitly incorporates image-aligned depth andedge priors derived from a single monocular RGB photograph. These ge-ometric cues are encoded via a lightweight Multi-Prior Feature Encoder(MFE) and injected into a frozen SAM backbone through adapter-basedinteraction blocks. For high-resolution archival imagery, we further adoptoverlapped sliding-window inference with Gaussian-weighted merging toensure spatial consistency. Extensive experiments demonstrate that whileadapting the frozen SAM image encoder with lightweight adapters es-tablishes a highly competitive pure-RGB baseline that outperforms stan-dard decoder-only fine-tuning, naively injecting a single geometric priorcan induce modality interference. Crucially, ReliefSAM’s joint integra-tion of depth and soft-edge priors achieves a geometric consensus inwhich the two priors counterbalance and neutralize prior-specific biases.This synergistic 2.5D guidance breaks the RGB-only performance ceil-ing, effectively bridging the gap between natural RGB appearance andheritage-specific structures without requiring large-scale retraining of thefoundation model.