Abstract Camouflaged object detection (COD) faces unique challenges due to the extremely high visual similarity between objects and their surroundings, coupled with indistinct boundary features. While the introduction of depth information has provided new insights into addressing these challenges, existing methods still exhibit considerable limitations in depth data quality assessment and optimization. To address this issue, this paper proposes a depth screening and calibration (DSC) framework aimed at constructing a high-quality RGBD COD dataset. The framework first establishes a comprehensive evaluation metric that quantitatively assesses depth data generated by various monocular depth estimation (MDE) methods across multiple dimensions, including structural similarity, edge consistency, foreground smoothness, depth value utilization, and depth disparity between foreground and background. Based on these metrics, optimal depth maps are selected from those generated by multiple MDE methods for each image, forming an initial RGBD COD dataset. Subsequently, a Two-stage Depth Calibration (TDC) strategy is designed to calibrate the depth maps in the initial dataset through two consecutive phases: positive-negative sample discrimination and calibrated depth map generation, effectively enhancing the overall quality of depth maps. Experimental results on three benchmark datasets demonstrate that detection models trained with our high-quality depth data significantly outperform alternative approaches. This work provides a reliable data foundation for further exploring the role of depth information in improving COD performance.
Fu et al. (Wed,) studied this question.