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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Updated: Jan 13, 2026

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Efficiency Evaluation of Sampling Density for Indoor Building LiDAR Point-Cloud Segmentation.

Yiquan Zou1, Wenxuan Chen1, Tianxiang Liang1

  • 1School of Civil Engineering, Architecture and the Environment, Hubei University of Technology, 28 Nanli Road, Wuhan 430068, China.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary

This study introduces a unified framework to optimize LiDAR point-cloud sampling density for indoor mapping. It identifies an ideal density range for balancing accuracy and efficiency in Scan-to-BIM workflows.

Keywords:
LiDARScan-to-BIMindoor building mappingoptimal density selectionpoint cloud semantic segmentationsampling densityuniform down-sampling

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Area of Science:

  • Computer Vision
  • Robotics
  • Geomatics Engineering

Background:

  • Indoor LiDAR point-cloud semantic segmentation accuracy is heavily influenced by sampling density, creating an accuracy-efficiency trade-off.
  • Current density selection methods are often heuristic and lack standardized protocols, limiting quantitative guidance for practical applications.

Purpose of the Study:

  • To develop a unified evaluation framework for analyzing the impact of sampling density on LiDAR point-cloud semantic segmentation.
  • To provide reproducible, model-agnostic guidance for optimizing density in indoor mapping and Scan-to-BIM (Scan-to-Building Information Modeling) workflows.

Main Methods:

  • A standardized protocol was used to evaluate three representative backbones (PointNet, PointNet++, DGCNN) augmented with a Point Transformer module.
  • The framework employed isotropic voxel-guided uniform down-sampling and a decision rule integrating accuracy sufficiency, efficiency, and accuracy-density curve analysis.
  • Experiments were conducted on indoor point clouds, contrasting scan-derived data with BIM-derived counterparts.

Main Results:

  • The study quantified the accuracy-runtime trade-off, identifying an engineering-feasible operating band of 1600-2900 points/m², with a robust setting around 2400 points/m².
  • Planar components showed saturation at moderate densities, while beam components were more sensitive to down-sampling.
  • The proposed framework offers reproducible guidance by isolating density effects and standardizing evaluation protocols.

Conclusions:

  • The research provides quantitative, reproducible, and model-agnostic guidance for scan planning and compute budgeting in indoor mapping.
  • The findings are crucial for optimizing resource allocation and improving the efficiency of Scan-to-BIM processes.
  • Establishing a standardized approach to density selection enhances the reliability and applicability of LiDAR-based indoor mapping solutions.