Researchers have introduced Any2Full, a novel one-stage framework for depth completion that reformulates the task as scale-prompting adaptation of a pre-trained monocular depth estimation model. This approach aims to improve domain generalization and robustness by avoiding the limitations of existing methods that rely on explicit relative-to-metric alignment. Any2Full utilizes a Scale-Aware Prompt Encoder to distill scale cues from sparse inputs into unified prompts, enabling globally scale-consistent predictions while preserving geometric priors. Experiments show Any2Full outperforms OMNI-DC in accuracy and is faster than PriorDA, establishing a new paradigm for universal depth completion. AI
IMPACT This new framework could improve robotic perception by enabling more accurate and efficient depth estimation from sparse sensor data.
RANK_REASON This is a research paper detailing a new method for depth completion. [lever_c_demoted from research: ic=1 ai=1.0]
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