PC-OPT: A SfM Point Cloud Denoising Algorithm

Yushi Li, George Baciu*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)


Different from 3D models created by digital scanning devices, Structure From Motion (SfM) models are represented by point clouds with much sparser distributions. Noisy points in these representations are often unavoidable in practical applications, specifically when the accurate reconstruction of 3D surfaces is required, or when object registration and classification is performed in deep convolutional neural networks. Outliers and deformed geometric structures caused by computational errors in the SfM algorithms have a significant negative impact on the postprocessing of 3D point clouds in object and scene learning algorithms, indoor localization and automatic vehicle navigation, medical imaging, and many other applications. In this paper, we introduce several new methods to classify the points generated by the SfM process. We present a novel approach, Point-Cloud Optimization (PC-OPT), that integrates density-based filtering and surface smoothing for handling noisy points, and maintains the geometric integrity. Furthermore, an improved moving least squares (MLS) is constructed to smooth out the SfM geometry with varying scales.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings
EditorsCesar Analide, Paulo Novais, David Camacho, Hujun Yin
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030623616
Publication statusPublished - 2020
Externally publishedYes
Event21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 - Guimaraes, Portugal
Duration: 4 Nov 20206 Nov 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12489 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020


  • 3D noise reduction
  • Density-based clustering
  • Outlier removing
  • SfM
  • Surface smoothing

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