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Digital twin-inspired geometry-aware monocular depth estimation for cluttered stacking parts
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DOI：10.1007/s10845-026-02952-x.png)
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Abstract
En 中文
Vision-based depth perception is essential for robotics embodiment, industrial inspection, and measurement. However, strong specular reflections, weak surface textures and severe occlusions of cluttered stacking parts make reliable depth perception particularly challenging. Existing depth perception methods often suffer from poor generalization due to unreliable photometric cues and the scarcity of annotated real-world depth data. To address these challenges, this paper proposes a digital twin-inspired geometry-aware monocular depth estimation framework for cluttered stacking parts. A calibrated digital twin is constructed through material and appearance modeling, pose estimation and scene reconstruction, as well as illumination modeling and relighting, enabling the generation of large-scale multimodal training data with precision geometric ground truth. Based on this data, a normal-guided depth estimation network is designed, where surface normals are explicitly embedded into the depth estimation process as transferable geometric priors through feature-level fusion. In addition, a geometry-aware virtual-to-real transfer strategy is introduced to adapt the pretrained model to real industrial images emphasizing geometric. Experimental results demonstrate that the proposed method achieves accurate and structurally consistent depth estimation for cluttered stacking parts and exhibits strong generalization performance, providing an effective solution for depth perception in complex industrial environments.
Keywords:
Digital twin-inspired data generation
Monocular depth estimation
Geometry-aware
Cluttered stacking scenes
Virtual-to-real transfer
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