3D Point Cloud Data |

PointSGRADE: Sparse Learning with Graph Representation for Anomaly Detection by Using Unstructured 3D Point Cloud Data

Mon, 01 Jan 2024 00:00:00 +0000

Assumption:

(1) Smooth free-form surface: limited overall curvature; neighborhood approximated well by local plane
(2) Sparse anomaly
(3) Gaussian measurement noise

Goal

Propose a computational efficient method for sparse anomaly detection of smooth free-form surface using one single point cloud sample.

Overall framework of PointSGRADE.

Formulation and graph representation for smooth free-form surface.

(a)

(b)

Anomaly Detection for Fabricated Artifact by Using Unstructured 3D Point Cloud Data

Wed, 01 Nov 2023 00:00:00 +0000

Types of Anomalies on the Steel Surface

Three types of anomalies on the steel surface:
- (a) pinhole
- (b) depression
- (c) oscillation mark
Three types of anomalies on the steel surface:
- (d) depression
- (e) pinhole
- (f) debris patch

Assumption:

(1) Globally smooth reference surface; Approximated by the B-spline surface with a parametric base plane
(2) Locally smooth anomaly
(3) Locally non-smooth outlier
(4) Gaussian measurement noise

Goal

Surface anomaly detection using one single point cloud sample.

Overall Framework

Overall framework of the model

Result

The proposed method is effective and robust to the different types of anomalies. (Grey: reference surface point; Blue: anomaly point; Red: outlier point.)

Real Case Study

The proposed method performs the best in real samples among the comparison methods.

(a)

(b)

A Tensor Voting-Based Surface Anomaly Classification Approach by Using 3D Point Cloud Data

Sun, 01 May 2022 00:00:00 +0000