To fully exploit depth cues in Camouflaged Object Detection (COD), we present DGA-Net, a specialized framework that adapts the Segment Anything Model (SAM) via a novel ``depth prompting" paradigm. Distinguished from existing approaches that primarily rely on sparse prompts (e.g., points or boxes), our method introduces a holistic mechanism for constructing and propagating dense depth prompts. Specifically, we propose a Cross-modal Graph Enhancement (CGE) module that synthesizes RGB semantics and depth geometric within a heterogeneous graph to form a unified guidance signal. Furthermore, we design an Anchor-Guided Refinement (AGR) module. To counteract the inherent information decay in feature hierarchies, AGR forges a global anchor and establishes direct non-local pathways to broadcast this guidance from deep to shallow layers, ensuring precise and consistent segmentation. Quantitative and qualitative experimental results demonstrate that our proposed DGA-Net outperforms the state-of-the-art COD methods.

翻译：为充分利用深度线索于伪装目标检测（COD）任务，我们提出了DGA-Net，这是一个通过新颖的“深度提示”范式来适配Segment Anything Model（SAM）的专用框架。与主要依赖稀疏提示（如点或框）的现有方法不同，我们的方法引入了一种用于构建和传播密集深度提示的整体机制。具体而言，我们提出了一个跨模态图增强（CGE）模块，该模块在异质图中融合RGB语义与深度几何信息，以形成统一的引导信号。此外，我们设计了一个锚点引导细化（AGR）模块。为抵消特征层次中固有的信息衰减，AGR构建了一个全局锚点，并建立了直接的非局部通路，将此引导信号从深层广播至浅层，从而确保精确且一致的分割。定量与定性的实验结果表明，我们提出的DGA-Net在性能上超越了当前最先进的COD方法。