High-Precision Dichotomous Image Segmentation via Depth Integrity-Prior and Fine-Grained Patch Strategy

• URL: http://arxiv.org/abs/2503.06100v4
• Date: Sun, 28 Sep 2025 08:14:20 GMT
• Title: High-Precision Dichotomous Image Segmentation via Depth Integrity-Prior and Fine-Grained Patch Strategy
• Authors: Xianjie Liu, Keren Fu, Qijun Zhao,
• Abstract summary: High-precision dichotomous image segmentation (DIS) is a task of extracting fine-grained objects from high-resolution images.<n>Existing methods face a dilemma: non-diffusion methods work efficiently but suffer from false or missed detections due to weak semantics.<n>We find pseudo depth information from monocular depth estimation models can provide essential semantic understanding.
• Score: 23.431898388115044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-precision dichotomous image segmentation (DIS) is a task of extracting fine-grained objects from high-resolution images. Existing methods face a dilemma: non-diffusion methods work efficiently but suffer from false or missed detections due to weak semantics and less robust spatial priors; diffusion methods, using strong generative priors, have high accuracy but encounter high computational burdens. As a solution, we find pseudo depth information from monocular depth estimation models can provide essential semantic understanding that quickly reveals spatial differences across target objects and backgrounds. Inspired by this phenomenon, we discover a novel insight we term the depth integrity-prior: in pseudo depth maps, foreground objects consistently convey stable depth values with much lower variances than chaotic background patterns. To exploit such a prior, we propose a Prior of Depth Fusion Network (PDFNet). Specifically, our network establishes multimodal interactive modeling to achieve depth-guided structural perception by deeply fusing RGB and pseudo depth features. We further introduce a novel depth integrity-prior loss to explicitly enforce depth consistency in segmentation results. Additionally, we design a fine-grained perception enhancement module with adaptive patch selection to perform boundary-sensitive detail refinement. Notably, PDFNet achieves state-of-the-art performance with only 94M parameters (<11% of those diffusion-based models), outperforming all non-diffusion methods and surpassing some diffusion methods. Code is provided in the supplementary materials.

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