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

2025-05-09

AI Summary

This work addresses the neglect of mechanical causality in 3D anomaly detection by proposing the first defect-force-source-based detection paradigm. Methodologically, it formalizes 3D anomalies as outcomes of internal/external defect forces and introduces MC4AD—a mechanics-complementary framework comprising DA-Gen (anomaly generation) and CFP-Net (corrective force prediction), augmented by a symmetry loss, three-way decision theory, and the Anomaly-IntraVariance benchmark for intra-class variance awareness. Contributions include: (1) the first fine-grained anomaly modeling grounded in mechanical principles; (2) unified detection and causal attribution via interpretable corrective force prediction; and (3) a three-stage quality control strategy enhancing robustness. The method achieves nine state-of-the-art results across six benchmarks, with the fewest parameters and fastest inference speed. Code is publicly available.

Technology Category

• Computer Vision: 3D Computer Vision
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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

Problem

• Detecting 3D anomalies by analyzing mechanical defect sources
• Simulating corrective forces to address internal and external anomalies
• Proposing a hierarchical quality control strategy for industrial 3D inspection

Innovation

• Mechanics Complementary framework for 3D anomaly detection
• Diverse Anomaly-Generation module simulates various anomalies
• Corrective Force Prediction Network with complementary representations