Any2Full 框架通过尺度提示适应提供单阶段深度补全

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-13 04:00

研究人员推出了一种新颖的单阶段深度补全框架 Any2Full，该框架将任务重新构建为预训练单目深度估计模型的尺度提示适应。该方法旨在通过避免依赖显式相对到度量对齐的现有方法的局限性来提高域泛化能力和鲁棒性。Any2Full 利用尺度感知提示编码器将稀疏输入中的尺度线索提炼成统一的提示，从而实现全局尺度一致的预测，同时保留几何先验。实验表明，Any2Full 在准确性方面优于 OMNI-DC，并且比 PriorDA 更快，为通用深度补全树立了新范例。 AI

影响该新框架可以通过从稀疏传感器数据中进行更准确、更高效的深度估计来改进机器人感知。

排序理由这是一篇详细介绍深度补全新方法的 ist 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Zhiyuan Zhou, Ruofeng Liu, Taichi Liu, Weijian Zuo, Shanshan Wang, Zhiqing Hong, Desheng Zhang · 2026-07-13 04:00

Any to Full: Prompting Depth Anything for Depth Completion in One Stage

arXiv:2603.05711v2 Announce Type: replace Abstract: Accurate, dense depth estimation is crucial for robotic perception, but commodity sensors often yield sparse or incomplete measurements due to hardware limitations. Existing RGBD-fused depth completion methods learn priors joint…

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Any to Full: Prompting Depth Anything for Depth Completion in One Stage

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