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

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.

Figures (10)

• Figure 1: Comparison between previous methods and our method in 3D anomaly synthesis. In (b), from top to bottom, the rows illustrate anomalies synthesized based on 1D, 2D, and 3D primitives, respectively.
• Figure 2: MPAS framework leverages different dimensional primitives to automatically synthesize massive, realistic, and diverse 3D anomalous data.
• Figure 3: Overview of the proposed Synthesis4AD system. Stage I parses product-side knowledge, including expert priors, multi-view cues, and textual specifications, into executable synthesis instructions via an MLLM, and drives 3D-DefectStudio to inject controllable anomalies into large-scale normal 3D assets. Stage II trains the anomaly detector using the generated anomalous samples and their ground-truth masks. Stage III deploys the trained model for prototype-based online inference, producing both point-wise anomaly maps and object-wise anomaly scores from scanned test data.
• Figure 4: Visualization of anomalies. From top to bottom: real anomalies, Synthesized anomalies by MPAS with the same types, and two rows of more diverse compound anomalies synthesized by MPAS. Red insets highlight defect regions for detailed comparison.
• Figure 5: t-SNE visualization of feature distributions. Normal samples and real anomalies are compared with synthetic anomalies generated by MPAS, GLFM, and R3D-AD.