Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection

2026-04-06

AI Summary

This work addresses the challenges of scarce anomaly samples and long-tailed distributions in industrial 3D anomaly detection. The authors propose Synthesis4AD, an end-to-end framework that pioneers the use of multimodal large language models to interpret design specifications and generate executable instructions for synthesizing anomalies. Coupled with MPAS—a primitive-guided, high-dimensional controllable synthesis engine—it enables large-scale generation of geometrically realistic anomalies with precise pixel-level masks. The framework further enhances training of Point Transformer detectors through spatial distribution normalization and geometry-preserving data augmentation strategies. Extensive experiments demonstrate state-of-the-art performance on Real3D-AD, MulSen-AD, and real-world industrial part datasets. Both the MPAS synthesis method and the 3D-DefectStudio platform will be publicly released.

Abstract

Industrial 3D anomaly detection performance is fundamentally constrained by the scarcity and long-tailed distribution of abnormal samples. To address this challenge, we propose Synthesis4AD, an end-to-end paradigm that leverages large-scale, high-fidelity synthetic anomalies to learn more discriminative representations for 3D anomaly detection. At the core of Synthesis4AD is 3D-DefectStudio, a software platform built upon the controllable synthesis engine MPAS, which injects geometrically realistic defects guided by higher-dimensional support primitives while simultaneously generating accurate point-wise anomaly masks. Furthermore, Synthesis4AD incorporates a multimodal large language model (MLLM) to interpret product design information and automatically translate it into executable anomaly synthesis instructions, enabling scalable and knowledge-driven anomalous data generation. To improve the robustness and generalization of the downstream detector on unstructured point clouds, Synthesis4AD further introduces a training pipeline based on spatial-distribution normalization and geometry-faithful data augmentations, which alleviates the sensitivity of Point Transformer architectures to absolute coordinates and improves feature learning under realistic data variations. Extensive experiments demonstrate state-of-the-art performance on Real3D-AD, MulSen-AD, and a real-world industrial parts dataset. The proposed synthesis method MPAS and the interactive system 3D-DefectStudio will be publicly released at https://github.com/hustCYQ/Synthesis4AD.

Problem

• 3D anomaly detection
• anomaly scarcity
• long-tailed distribution
• industrial inspection
• synthetic anomalies

Innovation

• synthetic anomalies
• 3D anomaly detection
• multimodal large language model
• geometry-faithful augmentation
• controllable synthesis