May 24, 2024Open Access

Transparent Object Depth Completion

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Randomized trial demonstrates improved depth completion in transparent objects, suggesting enhanced robotic grasping capabilities.

Achieving superior accuracy in depth completion significantly improves robotic manipulation of transparent objects.
The method fuses depth predictions from single-view and multi-view modules with a confidence estimation approach.
End-to-end network for depth completion effectively addresses challenges of transparent object perception and occlusion issues established in prior methods. This method outperforms existing state-of-the-art depth completion methods, especially in complex scenarios.

Zhou et al. (2024) studied this question.

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May 24, 2024Open Access

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Authors

Randomized trial demonstrates improved depth completion in transparent objects, suggesting enhanced robotic grasping capabilities.

Achieving superior accuracy in depth completion significantly improves robotic manipulation of transparent objects.
The method fuses depth predictions from single-view and multi-view modules with a confidence estimation approach.
End-to-end network for depth completion effectively addresses challenges of transparent object perception and occlusion issues established in prior methods. This method outperforms existing state-of-the-art depth completion methods, especially in complex scenarios.

Zhou et al. (2024) studied this question.

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