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Paper

Real-time monocular depth estimation on embedded devices: challenges and performances in terrestrial and underwater scenarios

by Independent / Community 0113be930e581137cd9ba7e7dc1279e96af4b06b

Free2AITools Nexus Index

62.3

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 68

P: Popularity 43

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Ahstract―The knowledge of the environmental depth is essen-tial in multiple robotics and computer vision tasks for both ter-restrial and underwater scenarios. Recent works aim at enabling depth perception using single RGB images on deep architectures, such as convolutional neural networks and vision transformers, which are generally unsuitable for real-time inference on low-power embedded hardwares. Moreover, such architectures are trained to estimate depth maps mainly on terrestrial scenario...

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Semantic Scholar 3 Citations

Paper Information Summary
Entity Passport
Registry ID	0113be930e581137cd9ba7e7dc1279e96af4b06b
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{0113be930e581137cd9ba7e7dc1279e96af4b06b,
  author = {Unknown},
  title = {Real-time monocular depth estimation on embedded devices: challenges and performances in terrestrial and underwater scenarios Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0113be930e581137cd9ba7e7dc1279e96af4b06b}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Real-time monocular depth estimation on embedded devices: challenges and performances in terrestrial and underwater scenarios [Paper]. Free2AITools. https://api.semanticscholar.org/0113be930e581137cd9ba7e7dc1279e96af4b06b

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 68

Popularity (P) 43

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Real-time monocular depth estimation on embedded devices: challenges and performances in terrestrial and underwater scenarios: Authority (A:68), Popularity (P:43), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

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📝 Executive Summary

"Ahstract―The knowledge of the environmental depth is essen-tial in multiple robotics and computer vision tasks for both ter-restrial and underwater scenarios. Recent works aim at enabling depth perception using single RGB images on deep architectures, such as convolutional neural networks and vision transformers, which are generally unsuitable for real-time inference on low-power embedded hardwares. Moreover, such architectures are trained to estimate depth maps mainly on terrestrial scenario..."

❝ Cite Node

@article{Unknown2026Real-time,
  title={Real-time monocular depth estimation on embedded devices: challenges and performances in terrestrial and underwater scenarios},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

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Read the full paper on arXiv

📊 Research Signals

📈3CitationsSemantic Scholar

🏛️68AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️knowledge retrievalField

🏷️ Research Topics

transformer architecturevision modelsimage generation

📦Data Source: semantic_scholar

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Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🛡️ Paper Transparency Report

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: 6
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: null
forks: null
citations: 3

Data indexed from public sources. Updated daily.