The recent advancements in anomaly detection research are primarily focused on enhancing robustness and adaptability to real-world scenarios, particularly in the face of distribution shifts and multi-object complexities. Innovations are being driven by the need to redefine normality at more granular levels, such as object-level definitions, and to develop methods that can effectively handle the inherent challenges of 3D data and multi-sensor inputs. Techniques like adaptive mask-inpainting, point offset learning, and coarse-knowledge-aware adversarial learning are emerging as powerful tools for improving detection accuracy and localization. Additionally, the integration of multi-sensor data is proving crucial for capturing a broader range of anomaly types, thereby advancing industrial quality inspection. Notable contributions include methods that redefine normality at the object level, adaptive mask-inpainting networks for industrial applications, and multi-sensor fusion algorithms that significantly enhance detection accuracy.