This paper introduces a novel anomaly detection (AD) problem that focuses on identifying `odd-looking' objects relative to the other instances within a scene. Unlike the traditional AD benchmarks, in our setting, anomalies in this context are scene-specific, defined by the regular instances that make up the majority. Since object instances are often partly visible from a single viewpoint, our setting provides 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 generates 3D object-centric representations for each instance and detects the anomalous ones through a cross-examination between the instances. We rigorously analyze our method quantitatively and qualitatively in the presented benchmarks.

翻译：本文提出了一种新颖的异常检测问题，其核心在于识别场景中相对于其他实例显得"异常"的物体。与传统异常检测基准不同，本设定中的异常具有场景特异性，由构成多数的常规实例所定义。由于从单一视角观察时物体实例往往部分可见，我们的设定为每个场景提供多视角输入作为数据源。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。我们创新性地提出一种方法，通过为每个实例生成以物体为中心的三维表征，并利用实例间的交叉比对机制检测异常样本。我们在所构建的基准数据集上，从定量与定性两个维度对方法进行了系统化验证与分析。