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

TL;DR

Synthesis4AD introduces a synthetic anomaly generation framework using 3D-DefectStudio and MLLM to enhance 3D anomaly detection, achieving state-of-the-art results on multiple datasets.

Contribution

The paper presents a scalable, knowledge-driven synthetic anomaly generation system for 3D data, improving detection robustness and generalization.

Findings

Achieves state-of-the-art results on Real3D-AD, MulSen-AD, and industrial datasets.

Introduces a controllable synthesis engine for realistic 3D defects.

Demonstrates improved robustness of Point Transformer models with new data augmentations.

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.