Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset
Jianjian Qin Chunzhi Gu Jun Yu Chao Zhang
We present PD-REAL, a novel large-scale dataset for unsupervised anomaly
detection (AD) in the 3D domain. It is motivated by the fact that 2D-only
representations in the AD task may fail to capture the geometric structures of
anomalies due to uncertainty in lighting conditions or shooting angles. PD-REAL
consists entirely of Play-Doh models for 15 object categories and focuses on
the analysis of potential benefits from 3D information in a controlled
environment. Specifically, objects are first created with six types of
anomalies, such as dent, crack, or perforation, and then photographed under
different lighting conditions to mimic real-world inspection scenarios. To
demonstrate the usefulness of 3D information, we use a commercially available
RealSense camera to capture RGB and depth images. Compared to the existing 3D
dataset for AD tasks, the data acquisition of PD-REAL is significantly cheaper,
easily scalable and easier to control variables. Extensive evaluations with
state-of-the-art AD algorithms on our dataset demonstrate the benefits as well
as challenges of using 3D information. Our dataset can be downloaded from
https://github.com/Andy-cs008/PD-REAL