Beyond Appearance: Camouflaged Object Detection via Geometric Structure

Jinyu Han*1, Changguang Wu*1, Fuming Sun2, Jinhui Tang3,

1Nanjing University of Science and Technology   2Dalian Minzu University   3Nanjing Forestry University

*Equal Contribution

📌CVPR 2026

Paper Code Model BibTex

Abstract

Depth priors provide salient geometric structure that benefits camouflaged object detection (COD), but directly using Monocular Depth Estimation (MDE) causes a task misalignment that still fails to identify camouflaged objects. To address this issue, we propose the Depth Segment Anything Model (DepthSAM), a MDE-adapted method specifically designed to mitigate this misalignment. DepthSAM incorporates two core innovations: (1) a Sparse Mixture-of-Experts Adapter (SMEA) that enables DEM to learn semantic information unique to camouflaged scenes, and (2) a Geometric–Semantic Fusion Module (GSFM) that efficiently integrates geometric cues with high-level semantics. With these components, DepthSAM achieves both robust semantic understanding in camouflaged environments and accurate segmentation of camouflaged objects. Extensive experiments show that DepthSAM achieves new SOTA performance on three major benchmarks. For example, on COD10K, its $S_{\alpha}$ and $F_{\beta}^{\omega}$ metrics surpass the best competing methods by 3.0% and 4.3%, respectively.

Methodology

Image Gallery

Slide 1

Results

Result Images

Table 1
Figure 3

BibTeX

        
        
          @inproceedings{han2026beyond,
            title={Beyond Appearance: Camouflaged Object Detection via Geometric Structure},
            author={Han, Jinyu and Wu, Changguang and Sun, Fuming and Tang, Jinhui},
            booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
            pages={25830--25840},
            year={2026}
          }