PoseNeRF: In Situ 3D Reconstruction Method Based on Joint Optimization of Pose and Neural Radiation Field for Smooth and Weakly Textured Aeroengine Blade.

Yao Xiao, Xin Wu, Yizhen Yin, Yu Cai, Yuanhan Hou

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yao XiaoNational Key Laboratory of Aerospace Power System and Plasma Technology, Air Force Engineering University, Xi'an 710038, China.

Xin WuNational Key Laboratory of Aerospace Power System and Plasma Technology, Air Force Engineering University, Xi'an 710038, China.

Yizhen YinInstitute of Aeronautics Engine, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Yu CaiNational Key Laboratory of Aerospace Power System and Plasma Technology, Air Force Engineering University, Xi'an 710038, China.

Yuanhan HouNational Key Laboratory of Aerospace Power System and Plasma Technology, Air Force Engineering University, Xi'an 710038, China.

Funding

National Natural Science Foundation of China 12075319National Science and Technology Major Project J2019-III-00009-0053

6 · The paper itself

Abstract

Digital twins are essential for the real-time health management and monitoring of aeroengines, and the in situ three-dimensional (3D) reconstruction technology of key components of aeroengines is an important support for the construction of a digital twin model. In this paper, an in situ high-fidelity 3D reconstruction method, named PoseNeRF, for aeroengine blades based on the joint optimization of pose and neural radiance field (NeRF), is proposed. An aeroengine blades background filtering network based on complex network theory (ComBFNet) is designed to filter out the useless background information contained in the two-dimensional (2D) images and improve the fidelity of the 3D reconstruction of blades, and the mean intersection over union (

Indexed as

3D reconstructionaeroengine bladesdeep learningindustrial borescopeneural radiance field

Identifiers

PMID41094973

DOI10.3390/s25196145

PMCPMC12526533

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.