On the Issue of Setting Up Model Training in the Classification and Segmentation of Point Clouds

The features and capabilities of the PointNet neural network architecture in relation to artificially generated clouds of laser reflection points in the Terra_Maker information system are presented. The results of training by the PointNet network are analyzed, and the accuracy of the obtained models and graphs is evaluated. An approach is proposed to determine the parameters providing maximum accuracy when performing experiments on the example of point clouds obtained from the Terra_Maker information system.

Authors

Institutions

Publication Details

Journal
Scientific and Technical Information Processing
Published
2025-12-01
DOI
https://doi.org/10.3103/s0147688225700339
Primary Topic
Advanced Research in Systems and Signal Processing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

On the Issue of Setting Up Model Training in the Classification and Segmentation of Point Clouds

D. A. Levchenko, R. A. Dyachenko, D. A. Gura, E. S. Boyko
Scientific and Technical Information Processing
Advanced Research in Systems and Signal Processing
article

On the Issue of Setting Up Model Training in the Classification and Segmentation of Point Clouds

D. A. Levchenko, R. A. Dyachenko, D. A. Gura, E. S. Boyko
article en

Abstract

The features and capabilities of the PointNet neural network architecture in relation to artificially generated clouds of laser reflection points in the Terra_Maker information system are presented. The results of training by the PointNet network are analyzed, and the accuracy of the obtained models and graphs is evaluated. An approach is proposed to determine the parameters providing maximum accuracy when performing experiments on the example of point clouds obtained from the Terra_Maker information system.

Scientific and Technical Information ProcessingVol. 52(5)
Kuban State University (RU), Kuban State Technological University (RU), Kuban State Agrarian University (RU)
Openalex Percentile: Top 30%
Advanced Research in Systems and Signal Processing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.