  Object anomaly detection is an important problem in the field of machine
vision and has seen remarkable progress recently. However, two significant
challenges hinder its research and application. First, existing datasets lack
comprehensive visual information from various pose angles. They usually have an
unrealistic assumption that the anomaly-free training dataset is pose-aligned,
and the testing samples have the same pose as the training data. However, in
practice, anomaly may exist in any regions on a object, the training and query
samples may have different poses, calling for the study on pose-agnostic
anomaly detection. Second, the absence of a consensus on experimental protocols
for pose-agnostic anomaly detection leads to unfair comparisons of different
methods, hindering the research on pose-agnostic anomaly detection. To address
these issues, we develop Multi-pose Anomaly Detection (MAD) dataset and
Pose-agnostic Anomaly Detection (PAD) benchmark, which takes the first step to
address the pose-agnostic anomaly detection problem. Specifically, we build MAD
using 20 complex-shaped LEGO toys including 4K views with various poses, and
high-quality and diverse 3D anomalies in both simulated and real environments.
Additionally, we propose a novel method OmniposeAD, trained using MAD,
specifically designed for pose-agnostic anomaly detection. Through
comprehensive evaluations, we demonstrate the relevance of our dataset and
method. Furthermore, we provide an open-source benchmark library, including
dataset and baseline methods that cover 8 anomaly detection paradigms, to
facilitate future research and application in this domain. Code, data, and
models are publicly available at https://github.com/EricLee0224/PAD.
