  Detecting anomalies in images has become a well-explored problem in both
academia and industry. State-of-the-art algorithms are able to detect defects
in increasingly difficult settings and data modalities. However, most current
methods are not suited to address 3D objects captured from differing poses.
While solutions using Neural Radiance Fields (NeRFs) have been proposed, they
suffer from excessive computation requirements, which hinder real-world
usability. For this reason, we propose the novel 3D Gaussian splatting-based
framework SplatPose which, given multi-view images of a 3D object, accurately
estimates the pose of unseen views in a differentiable manner, and detects
anomalies in them. We achieve state-of-the-art results in both training and
inference speed, and detection performance, even when using less training data
than competing methods. We thoroughly evaluate our framework using the recently
proposed Pose-agnostic Anomaly Detection benchmark and its multi-pose anomaly
detection (MAD) data set.
