Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection

In this paper, we go beyond identifying anomalies only in structural terms and think about better anomaly detection motivated by anomaly causes. Most anomalies are regarded as the result of unpredictable defective forces from internal and external sources, and their opposite forces are sought to correct the anomalies. We introduced a Mechanics Complementary framework for 3D anomaly detection (MC4AD) to generate internal and external Corrective forces for each point. A Diverse Anomaly-Generation (DA-Gen) module is first proposed to simulate various anomalies. Then, we present a Corrective Force Prediction Network (CFP-Net) with complementary representations for point-level representation to simulate the different contributions of internal and external corrective forces. A combined loss was proposed, including a new symmetric loss and an overall loss, to constrain the corrective forces properly. As a highlight, we consider 3D anomaly detection in industry more comprehensively, creating a hierarchical quality control strategy based on a three-way decision and contributing a dataset named Anomaly-IntraVariance with intraclass variance to evaluate the model. On the proposed and existing five datasets, we obtained nine state-of-the-art performers with the minimum parameters and the fastest inference speed. The source is available at https://github.com/hzzzzzhappy/MC4AD