Depth-Dominant Skeleton Detection for Natural Scenes

• URL: http://arxiv.org/abs/2608.16367v1
• Date: Mon, 17 Aug 2026 10:17:46 GMT
• Title: Depth-Dominant Skeleton Detection for Natural Scenes
• Abstract summary: We observe that depth images are inherently insensitive to color and texture, and can provide clear regional contours and inter-region relationships.<n>Motivated by this observation, this paper proposes for the first time a novel skeleton detection paradigm where depth images serve as the dominant modality and RGB images act as spatial auxiliary.
• Score: 14.451705133115624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To date, all natural scene skeleton detection follows the paradigm of taking RGB images as the sole input; despite notable progress, methods under this paradigm suffer significant performance degradation on complex-content images. We observe that depth images are inherently insensitive to color and texture, and can provide clear regional contours and inter-region spatial relationships, which naturally alleviates the difficulty of skeleton detection in complex scenarios. Motivated by this observation, this paper proposes for the first time a novel skeleton detection paradigm where depth images serve as the dominant modality and RGB images act as the auxiliary, and accordingly presents a model DDSkel (short for Depth-Dominant Skeleton Detection) under this paradigm. DDSkel employs an asymmetric encoder design to fuse RGB information into depth features, with the RGB modality branch having only 12% the parameters of the depth modality branch. DDSkel has a simple structure without intricate designs. Nevertheless, with only 36% of the trainable parameters of the current best method, DDSkel outperforms all state-of-the-art approaches on SymPASCAL, the most challenging dataset with a large volume of complex images.

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