TY - GEN
T1 - PC-OPT
T2 - 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020
AU - Li, Yushi
AU - Baciu, George
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - 3D noise reduction
KW - Density-based clustering
KW - Outlier removing
KW - SfM
KW - Surface smoothing
UR - http://www.scopus.com/inward/record.url?scp=85097376690&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-62362-3_25
DO - 10.1007/978-3-030-62362-3_25
M3 - Conference Proceeding
AN - SCOPUS:85097376690
SN - 9783030623616
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 280
EP - 291
BT - Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings
A2 - Analide, Cesar
A2 - Novais, Paulo
A2 - Camacho, David
A2 - Yin, Hujun
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 November 2020 through 6 November 2020
ER -