  RGB-based surface anomaly detection methods have advanced significantly.
However, certain surface anomalies remain practically invisible in RGB alone,
necessitating the incorporation of 3D information. Existing approaches that
employ point-cloud backbones suffer from suboptimal representations and reduced
applicability due to slow processing. Re-training RGB backbones, designed for
faster dense input processing, on industrial depth datasets is hindered by the
limited availability of sufficiently large datasets. We make several
contributions to address these challenges. (i) We propose a novel Depth-Aware
Discrete Autoencoder (DADA) architecture, that enables learning a general
discrete latent space that jointly models RGB and 3D data for 3D surface
anomaly detection. (ii) We tackle the lack of diverse industrial depth datasets
by introducing a simulation process for learning informative depth features in
the depth encoder. (iii) We propose a new surface anomaly detection method
3DSR, which outperforms all existing state-of-the-art on the challenging
MVTec3D anomaly detection benchmark, both in terms of accuracy and processing
speed. The experimental results validate the effectiveness and efficiency of
our approach, highlighting the potential of utilizing depth information for
improved surface anomaly detection.
