DiBME-Net: Mirror-Boundary-Guided Diffusion-Band Feature Enhancement for RGB-D Mirror Segmentation

Mirror segmentation is challenging because reflected contours can be difficult to distinguish from physical mirror boundaries. Depth provides complementary geometric cues, but active sensing can produce invalid or distorted measurements on reflective surfaces. Diffusion-based feature aggregation with a heat conduction operator (HCO) provides global context for mirror localization, while boundary-relevant high-frequency structure is attenuated and remains in the complementary residual. We propose the Diffusion-Band Mirror Feature Enhancement Network (DiBME-Net), which retains the diffusion representation for global mirror localization while recovering and spatially modulating the complementary residual under learned mirror-boundary guidance. Its core module, Mirror-Boundary-Guided Diffusion-Band Feature Enhancement (MB-DBFE), consists of two components. First, HCO-Derived Diffusion Feature Decomposition (DFD) partitions the residual into diffusion-derived bands using differences between heat kernels defined by the same learned diffusion strength used for context aggregation, requiring no manually specified frequency cutoffs. Second, Boundary-Guided Residual Recovery (BRR) maintains a full-residual recovery path and spatially modulates the selected band responses using stage-aligned, band-specific boundary guidance. Because raw depth discontinuities also occur at ordinary object edges and can result from sensor artifacts, a Mirror Boundary Estimation (MBE) head predicts guidance from fused RGB-D features and is supervised using boundaries derived from the ground-truth mask. Controlled experiments support these design choices in terms of band derivation, band composition, boundary guidance, and stage placement. On RGBD-Mirror, DiBME-Net achieves an IoU of 84.82, an MAE of 0.025, and a BER of 6.09, all averaged over three random seeds, yielding the best reported IoU, MAE, and BER among the compared methods. It also transfers to multiclass transparent/reflective object segmentation on TROSD while retaining the proposed decomposition and boundary-guided recovery design.