Outlier detection is a critical task in time series analysis, essential to maintaining data quality and allowing for accurate subsequent analysis. The Hampel filter, a decision filter that replaces outliers in a data window with the median, is widely used for outlier detection in time series due to its simplicity and effectiveness. While effective, its computational complexity, primarily due to the calculation of the Median Absolute Deviation (MAD), poses limitations for large-scale and real-time applications. This paper proposes a novel Hampel filter variant that replaces the MAD with an original estimator (mMAD) that retains statistical robustness but is computationally more efficient. This reduces the filter’s computational complexity from $O(N·wlogw)$ to $O(N·w)$, where N is the data length and w the window size. The proposed variant significantly lowers processing time and resource consumption, making it especially suitable for large-scale and real-time data processing while preserving robust outlier detection performance.

In the manufacturing industry, inspection systems play a crucial role in ensuring product quality. High-resolution profilometric sensors have become increasingly popular for inspection due to their ability to provide detailed surface information. However, the development and testing of inspection systems can be costly and time-consuming. This paper presents the development of a simulation of an inspection system using a high-resolution profilometric sensor. A geometrical and noise model is proposed to simulate the readings of any actual profilometric sensor. The model replicates the sensor’s movement on the CAD model of the inspected part. The model incorporates the physical properties of the sensor and combines noise sources from sensor uncertainty and speckle noise induced by the roughness of the material. Our contribution lies in noise modeling. This work proposes a combination of Perlin noise to simulate the speckle noise and Gaussian noise for the uncertainty-related noise. Perlin noise is generated based on the surface roughness parameters of the inspected part. The accuracy of the simulation system is evaluated by comparing the simulated scans with real scans. The results highlight the ability to simulate real scans of different parts, using commercial sensor specifications and the CAD model of the inspected part.

High-precision surface defect detection in manufacturing often relies on laser triangulation profilometric sensors for detailed surface measurements, providing detailed and accurate surface measurements over a line. Accurate motion between the sensor and workpiece, usually managed by robotic systems, is critical for maintaining optimal distance and orientation. This paper introduces a novel Reinforcement Learning (RL) approach to optimize inspection trajectories for profilometric sensors based on the boustrophedon scanning method. The RL model dynamically adjusts sensor position and tilt to ensure consistent profile distribution and high-quality scanning. We use a simulated environment replicating real-world conditions, including sensor noise and surface irregularities, to plan trajectories offline using CAD models. Key contributions include designing a state space, action space, and reward function tailored for profilometric sensor inspection. The Proximal Policy Optimization (PPO) algorithm trains the RL agent to optimize these trajectories effectively. Validation involves testing the model on various parts in simulation and performing real-world inspection with a UR3e robotic arm, demonstrating the approach’s practicality and effectiveness.

Ensuring the surface quality of industrial components requires the detection of small superficial defects—such as cracks, bumps, and peaks—using high-resolution 3D sensors. However, training machine learning algorithms for this task is constrained by the limited availability of annotated 3D defect datasets. In this work, we propose a method for generating synthetic 3D datasets of surface defects using Free-Form Deformation applied to CAD models. The technique allows localized insertion of parametrized defects adapted to the object’s geometry and supports diverse defect types through customizable elevation maps. To simulate realistic sensor acquisition, we replicate the scanning process of a profilometric 3D sensor, including surface and sensor noise. The output consists of labeled depth images that closely resemble real-world sensor data. We validate the approach by training object detection networks on synthetic datasets and evaluating their performance on real samples, demonstrating comparable accuracy. The proposed method reduces reliance on rare and costly real defect data, offering a scalable tool for developing and testing surface inspection systems in industrial contexts.

Noise contamination in three-dimensional data remains a persistent challenge in reconstruction and measurement pipelines, with critical implications for dimensional inspection and industrial metrology, as well as for autonomous navigation and cultural heritage documentation. Whenever a point cloud is used to measure rather than merely to visualize, the noise it carries propagates directly into the reported dimension and into its associated uncertainty. This work presents an innovative three-axis taxonomy that unifies the classification and mitigation of noise in 3D point clouds. Our framework systematically categorizes noise sources into (1) statistical models, (2) scene interferences, and (3) reconstruction imperfections, and it is designed so that any newly encountered noise type can be assigned consistently by means of three explicit criteria (origin, modeling tractability and spatio-temporal dependency). Through theoretical analysis along these three axes, we discuss the fundamental limitations, practical challenges, and adaptive strategies of each method and summarize the resulting cross-category applicability in a comparative table. Among the unresolved challenges are real-time processing of dynamic scenes, separation of overlapping noise types, and biases in learning models toward specific sensor geometries. We further argue that, in a measurement context, a denoising operator must be assessed not only by the noise it suppresses but also by the systematic surface displacement—the bias—it introduces, which aggregate distance metrics tend to conceal. This research consolidates fragmented knowledge into actionable guidelines, equipping metrology and inspection practitioners to design robust 3D measurement systems and guiding researchers toward impactful innovations in noise-adaptive sensing.