High-throughput and accurate cross-scale 3D sensing with a meta-depth sensor

Clicks: 28
ID: 326276
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #221 of 427 articles by views in national science review

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 427 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Abstract Depth sensing has been widely applied in industrial inspection, transportation, and consumer electronics. However, existing methods still face an inherent trade-off between depth precision and data throughput. Here, we present a meta-imaging-empowered depth sensor (MDS), a fully passive and monocular modality, that transcends this limitation through aberration robust disparity-depth calibration and vision foundation model empowered fine-gained 3D ranging based on a scanning light-field imaging system. Our system synergizes high-throughput disparity estimation and massive data prior of foundation model, enabling high-precision fine-grained metric depth estimation across depth ranges from 0.1 meter to 500 meters. Experimental validation demonstrates 2.5-mm absolute depth precision at 1.5-m distance (0.18% relative error) and 3-megapixel resolution, outperforming conventional light-field depth sensors by 7 times in accuracy and 5 times in spatial resolution without sacrificing imaging speed with a virtual scanning technique. We also achieve turbulence-robust remote depth sensing at the 500-m distance with a significantly denser spatial sampling rate compared with commercial LiDAR systems, and a depth estimation precision of 0.7 m at 235 m distance. By unifying the throughput advantages of passive imaging with high-precision depth estimation ability of physics-aware neural regression, the proposed MDS establishes a scalable paradigm for high-fidelity passive 3D sensing across scales, with applications ranging from industrial inspection to autonomous driving.
Reference Key
openalex_W7203963815 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors
Journal national science review
Year 2026
DOI
10.1093/nsr/nwag524
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.