Learning Diffusion Models for Multi-View Anomaly Detection

We are exploring an emerging formulation in anomaly detection (AD) where multiple instances of the same object are produced simultaneously and distinctly to address the limitation that using only a single instance may not effectively capture any underlying defects. More specifically, we concentrate on a specific scenario where each object of interest is linked to seven distinct data views/representations. The first six views involve capturing images with a stationary camera under six different lighting conditions, while the seventh view pertains to the 3D normal information. We refer to our intended task as multi-view anomaly detection. To tackle this problem, our approach involves training a view-invariant ControlNet that can produce consistent feature maps regardless of the data views. This training strategy enables us to mitigate the impact of varying lighting conditions and to fuse information from both the RGB color appearance and the 3D normal geometry effectively. Moreover, as the diffusion process is not deterministic, we utilize the DDIM scheme to improve the applicability of our established memory banks of diffusion-based features for anomaly detection inference. To demonstrate the efficacy of our approach, we present extensive ablation studies and state-of-the-art experimental results on the Eyecandies dataset.