  Anomaly detection is a critical problem in the manufacturing industry. In
many applications, images of objects to be analyzed are captured from multiple
perspectives which can be exploited to improve the robustness of anomaly
detection. In this work, we build upon the deep support vector data description
algorithm and address multi-perspective anomaly detection using three different
fusion techniques, i.e., early fusion, late fusion, and late fusion with
multiple decoders. We employ different augmentation techniques with a denoising
process to deal with scarce one-class data, which further improves the
performance (ROC AUC $= 80\%$). Furthermore, we introduce the dices dataset,
which consists of over 2000 grayscale images of falling dices from multiple
perspectives, with 5\% of the images containing rare anomalies (e.g., drill
holes, sawing, or scratches). We evaluate our approach on the new dices dataset
using images from two different perspectives and also benchmark on the standard
MNIST dataset. Extensive experiments demonstrate that our proposed
{multi-perspective} approach exceeds the state-of-the-art {single-perspective
anomaly detection on both the MNIST and dices datasets}. To the best of our
knowledge, this is the first work that focuses on addressing multi-perspective
anomaly detection in images by jointly using different perspectives together
with one single objective function for anomaly detection.
