Compressing and Recovering Short-Range MEMS-Based LiDAR Point Clouds Based on Adaptive Clustered Compressive Sensing and Application to 3D Rock Fragment Surface Point Clouds.

Lin Li, Huajun Wang, Sen Wang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Lin LiKey Laboratory of Earth Exploration and Infomation Techniques of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China.ORCID 0000-0002-5495-9503

Huajun WangCollege of Mathematics and Physics, Chengdu University of Technology, Chengdu 610059, China.

Sen WangDepartment of Information and Communication Engineering, Tongji University, Shanghai 200092, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Short-range MEMS-based (Micro Electronical Mechanical System) LiDAR provides precise point cloud datasets for rock fragment surfaces. However, there is more vibrational noise in MEMS-based LiDAR signals, which cannot guarantee that the reconstructed point cloud data are not distorted with a high compression ratio. Many studies have illustrated that wavelet-based clustered compressive sensing can improve reconstruction precision. The

Indexed as

clustered compressive sensinglocal clusteringMEMS-based LiDARrock fragment surface 3D point cloudvibrational signals

Identifiers

PMID39275606

DOI10.3390/s24175695

PMCPMC11398263

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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.