  We present Eyecandies, a novel synthetic dataset for unsupervised anomaly
detection and localization. Photo-realistic images of procedurally generated
candies are rendered in a controlled environment under multiple lightning
conditions, also providing depth and normal maps in an industrial conveyor
scenario. We make available anomaly-free samples for model training and
validation, while anomalous instances with precise ground-truth annotations are
provided only in the test set. The dataset comprises ten classes of candies,
each showing different challenges, such as complex textures, self-occlusions
and specularities. Furthermore, we achieve large intra-class variation by
randomly drawing key parameters of a procedural rendering pipeline, which
enables the creation of an arbitrary number of instances with photo-realistic
appearance. Likewise, anomalies are injected into the rendering graph and
pixel-wise annotations are automatically generated, overcoming human-biases and
possible inconsistencies.
  We believe this dataset may encourage the exploration of original approaches
to solve the anomaly detection task, e.g. by combining color, depth and normal
maps, as they are not provided by most of the existing datasets. Indeed, in
order to demonstrate how exploiting additional information may actually lead to
higher detection performance, we show the results obtained by training a deep
convolutional autoencoder to reconstruct different combinations of inputs.
