BED-SAM2: Boundary-Enhanced-Depth SAM2 via Monocular Geometric Priors

Abstract

Building upon the SAM2 vision foundation model for downstream segmentation, this study introduces Boundary Enhanced Depth (BED)-SAM2. The SAM2 Hiera encoder architecture is modified to directly encode monocular depth information from RGB images, thereby providing geometric cues that enhance object boundary delineation and facilitate the extraction of camouflaged object shapes. BED-SAM2 demonstrates competitive state-of-the-art performance across multiple salient and camouflaged object detection tasks with as few as five training epochs.

9 pages, 5 figures, 5 tables. Presented as a poster at the CVPR 2026 Workshop on Computer Vision in the Wild (CVinW). Code available at https://github.com/TylerRust-1/BED-SAM2