Phase Consistency Prior Driven RGB-D Salient Object Detection

For RGB-D salient object detection (SOD), a fundamental challenge lies in establishing effective cross-modality interactions between the input graphic domain (RGB and depth modalities) and the output saliency domain. While existing deep learning methods primarily focus on modeling image-level consistency through carefully designed feature extraction and fusion modules, this paper reveals a crucial discovery: the discrepancy in the phase component between the graphic and saliency domains can be reasonably approximated by a Laplacian distribution. Inspired by this observation, we establish a RGB-D-Saliency phase consistency model for RGB-D SOD, which explicitly formulates the cross-modality relationship from a phase perspective. Building upon this model, we develop a novel Phase Updating and Transform Network (PUTNet). It performs two key operations including (a) updating the saliency phase component under RGB-D-Saliency phase consistency constraints, and (b) generating the final saliency map through global phase-to-image transform. Extensive experiments across nine different RGB-D SOD datasets demonstrate that PUTNet outperforms state-of-the-art methods in both quantitative and qualitative evaluations.