This paper introduces a novel anomaly detection (AD) problem that focuses on identifying `odd-looking' objects relative to the other instances in a given scene. In contrast to the traditional AD benchmarks, anomalies in our task are scene-specific, defined by the regular instances that make up the majority. Since object instances may be only partly visible from a single viewpoint, our setting employs multiple views of each scene as input. To provide a testbed for future research in this task, we introduce two benchmarks, ToysAD-8K and PartsAD-15K. We propose a novel method that constructs 3D object-centric representations from multiple 2D views for each instance and detects the anomalous ones through a cross-instance comparison. We rigorously analyze our method quantitatively and qualitatively on the presented benchmarks.

翻译：本文提出了一种新颖的异常检测问题，其核心在于识别给定场景中相对于其他实例显得"异常"的对象。与传统异常检测基准不同，本任务中的异常具有场景特异性，由构成多数的常规实例所定义。由于从单一视角可能仅能观察到对象实例的部分信息，本设定采用每个场景的多视角图像作为输入。为构建该任务的未来研究测试平台，我们提出了两个基准数据集：ToysAD-8K与PartsAD-15K。我们设计了一种创新方法，通过多视角二维图像构建以对象为中心的三维表征，并通过跨实例比较实现异常检测。我们在所提基准数据集上从定量与定性两个维度对本方法进行了系统分析。