Image-based Pose-Agnostic 3D Anomaly Detection is an important task that has emerged in industrial quality control. This task seeks to find anomalies from query images of a tested object given a set of reference images of an anomaly-free object. The challenge is that the query views (a.k.a poses) are unknown and can be different from the reference views. Currently, new methods such as OmniposeAD and SplatPose have emerged to bridge the gap by synthesizing pseudo reference images at the query views for pixel-to-pixel comparison. However, none of these methods can infer in real-time, which is critical in industrial quality control for massive production. For this reason, we propose SplatPose+, which employs a hybrid representation consisting of a Structure from Motion (SfM) model for localization and a 3D Gaussian Splatting (3DGS) model for Novel View Synthesis. Although our proposed pipeline requires the computation of an additional SfM model, it offers real-time inference speeds and faster training compared to SplatPose. Quality-wise, we achieved a new SOTA on the Pose-agnostic Anomaly Detection benchmark with the Multi-Pose Anomaly Detection (MAD-SIM) dataset.
基于图像的姿态无关3D异常检测是工业质量控制中出现的一项重要任务。该任务旨在通过一组无异常物体的参考图像，从待测物体的查询图像中发现异常。其挑战在于查询视角（即姿态）未知，且可能与参考视角不同。目前，诸如OmniposeAD和SplatPose等新方法通过在查询视角下合成伪参考图像进行像素级对比，试图缩小差距。然而，这些方法均无法实现实时推断，而实时性在大规模生产的工业质量控制中至关重要。为此，我们提出了SplatPose+，该方法采用了混合表示形式，结合运动结构（SfM）模型进行定位，并利用3D高斯点云（3DGS）模型进行新视角合成。尽管我们的方法需要额外计算SfM模型，但相比SplatPose，它在推断速度和训练速度上更具实时性。质量方面，我们在姿态无关异常检测基准测试中，利用Multi-Pose Anomaly Detection (MAD-SIM) 数据集，达到了新的SOTA（最先进技术）水平。