Evidence map›Paper›PMID 36617011›Full record

ArticleSensors (Basel, Switzerland)2022

Robust Mesh Segmentation Using Feature-Aware Region Fusion.

Lulu Wu, Yu Hou, Junli Xu, Yong Zhao

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lulu WuSchool of Mathematical Sciences, Ocean University of China, Qingdao 266100, China.
Yu HouSchool of Mathematical Sciences, Ocean University of China, Qingdao 266100, China.
Junli XuSchool of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China.
Yong ZhaoSchool of Mathematical Sciences, Ocean University of China, Qingdao 266100, China.

Funding

National Natural Science Foundation of China 41606006Natural Science Foundation of Shandong Province ZR2018MF006Open Project of State Key Lab of CAD&CG, Zhejiang University A2228
6 · The paper itself

Abstract

This paper introduces a simple but powerful segmentation algorithm for 3D meshes. Our algorithm consists of two stages: over-segmentation and region fusion. In the first stage, adaptive space partition is applied to perform over-segmentation, which is very efficient. In the second stage, we define a new intra-region difference, inter-region difference, and fusion condition with the help of various shape features and propose an iterative region fusion method. As the region fusion process is feature aware, our algorithm can deal with complex 3D meshes robustly. Massive qualitative and quantitative experiments also validate the advantages of the proposed algorithm.

Indexed as

3D meshesfeature-aware region fusionrobust segmentation

Identifiers

PMID36617011
PMCPMC9824490

What OpenQuestion holds

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Registered trials

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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.