What question did this study set out to answer?

The aim is to develop an effective method to remove outliers from point clouds to enhance data quality.

June 3, 2026

SIGnificant Outlier Removal

Key Points

The aim is to develop an effective method to remove outliers from point clouds to enhance data quality.
Introduced a new proximity-based outlier removal method using spheres-of-influence.
Compared the performance of the new method with classical statistical methods.
Eliminated the need for multiple parameters typical of traditional methods.
The new method significantly improved data quality with minimal noise and outlier distortion.
Achieved better outcomes in normal estimation and surface reconstruction compared to traditional methods.

Abstract

Point clouds, usually obtained through scanning or various image processing, are commonly affected by noise and outliers. Such artifacts compromise data quality as they significantly distort subsequent processes, such as normal estimation and surface reconstruction. In this work, we introduce a proximity-based outlier removal method for point clouds. We improve on statistical methods based on neighboring graphs by using a parameter-free proximity graph—the spheres-of-influence (SIG), thus requiring fewer parameters compared to classical methods and obtaining better results. Moreover, the simplicity of our method allows it to become an easy replacement for existing statistical methods.

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