Evidence Receipt. Related Resources.
Any to Full: Prompting Depth Anything for Depth Completion in One Stage
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Canonical route: /signal-canvas/any-to-full-prompting-depth-anything-for-depth-completion-in-one-stage
- Proof freshness
- stale
- Proof status
- unverified
- Display score
- 8/10
- Last proof check
- 2026-04-02
- Score updated
- 2026-04-02
- Score fresh until
- 2026-05-02
- References
- 0
- Source count
- 0
- Coverage
- 17%
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Agent Handoff
Any to Full: Prompting Depth Anything for Depth Completion in One Stage
Canonical ID any-to-full-prompting-depth-anything-for-depth-completion-in-one-stage | Route /signal-canvas/any-to-full-prompting-depth-anything-for-depth-completion-in-one-stage
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/any-to-full-prompting-depth-anything-for-depth-completion-in-one-stageMCP example
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"query_text": "Summarize Any to Full: Prompting Depth Anything for Depth Completion in One Stage"
}
}source_context
{
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"query": "Any to Full: Prompting Depth Anything for Depth Completion in One Stage",
"normalized_query": "2603.05711",
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"paper_ref": "any-to-full-prompting-depth-anything-for-depth-completion-in-one-stage",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Preparing verified analysis
Dimensions overall score 8.0
GitHub Code Pulse
No public code linked for this paper yet.
Claim map
- Evidencepartial
It outperforms OMNI-DC by 32.2% in average AbsREL
ImplicationpartialA specific quantitative performance metric is provided in the abstract.
Verificationpartialpartial
- Evidencepartial
and delivers a 1.4x speedup over PriorDA with the same MDE backbone
ImplicationpartialA specific quantitative efficiency metric is provided in the abstract.
Verificationpartialpartial
- Evidencepartial
Any2Full, a one-stage, domain-general, and pattern-agnostic framework
ImplicationpartialThe abstract explicitly states 'Any2Full, a one-stage... framework'.
Verificationpartialpartial
- Evidencepartial
we present Any2Full... that reformulates completion as a scale-prompting adaptation of a pretrained MDE model.
ImplicationpartialThe abstract clearly describes the core mechanism of the proposed method.
Verificationpartialpartial
- Evidencepartial
To address varying depth sparsity levels and irregular spatial distributions, we design a Scale-Aware Prompt Encoder. It distills scale cues from sparse inputs into unified scale prompts
ImplicationpartialThe abstract details the function of the proposed encoder.
Verificationpartialpartial
- Evidencepartial
Extensive experiments demonstrate that Any2Full achieves superior robustness and efficiency.
ImplicationpartialThe abstract states this as a key finding from experiments.
Verificationpartialpartial
- Evidencepartial
Existing RGBD-fused depth completion methods learn priors jointly conditioned on training RGB distribution and specific depth patterns, limiting domain generalization and robustness to various depth patterns.
ImplicationpartialThe abstract identifies limitations of prior work as motivation for the new method.
Verificationpartialpartial
- Evidencepartial
current two-stage integration strategies relying on explicit relative-to-metric alignment incur additional computation and introduce structured distortions.
ImplicationpartialThe abstract highlights drawbacks of previous integration approaches.
Verificationpartialpartial
