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

TL;DR

This paper introduces a novel 3D anomaly detection framework based on mechanical forces, utilizing a model that generates and corrects anomalies through internal and external forces, achieving state-of-the-art results.

Contribution

The paper presents the MC4AD framework, combining anomaly simulation, force prediction, and hierarchical quality control, along with a new dataset for 3D anomaly detection evaluation.

Findings

Achieves nine state-of-the-art performances across five datasets.

Provides the fastest inference speed with minimal parameters.

Demonstrates effectiveness through theoretical analysis and experiments.

Abstract

In this paper, we explore a novel approach to 3D anomaly detection (AD) that goes beyond merely identifying anomalies based on structural characteristics. Our primary perspective is that most anomalies arise from unpredictable defective forces originating from both internal and external sources. To address these anomalies, we seek out opposing forces that can help correct them. Therefore, we introduce the Mechanics Complementary Model-based Framework for the 3D-AD task (MC4AD), which generates internal and external corrective forces for each point. We first propose a Diverse Anomaly-Generation (DA-Gen) module designed to simulate various types of anomalies. Next, we present the Corrective Force Prediction Network (CFP-Net), which uses complementary representations for point-level analysis to simulate the different contributions from internal and external corrective forces. To ensure the corrective forces are constrained effectively, we have developed a combined loss function that includes a new symmetric loss and an overall loss. Notably, we implement a Hierarchical Quality Control (HQC) strategy based on a three-way decision process and contribute a dataset titled Anomaly-IntraVariance, which incorporates intraclass variance to evaluate our model. As a result, the proposed MC4AD has been proven effective through theory and experimentation. The experimental results demonstrate that our approach yields nine state-of-the-art performances, achieving optimal results with minimal parameters and the fastest inference speed across five existing datasets, in addition to the proposed Anomaly-IntraVariance dataset. The source is available at https://github.com/hzzzzzhappy/MC4AD