🤖 AI Summary
This work addresses the high computational cost and inference latency of existing diffusion-based 3D point cloud anomaly detection methods, which hinder deployment on edge devices. The authors propose a novel paradigm based on consistency models that directly predicts anomaly-free geometric structures in just one or two forward passes. A reconstruction-oriented hybrid loss function is introduced to guide the model toward learning normal patterns effectively. The proposed approach substantially reduces both inference steps and computational overhead, achieving up to an 80-fold speedup over the current state-of-the-art method under CPU-only conditions. It attains competitive detection performance with I-AUROC scores of 76.20% on Anomaly-ShapeNet and 72.80% on Real3DAD, demonstrating a strong balance between efficiency and accuracy.
📝 Abstract
Diffusion models are rapidly redefining 3D anomaly detection in point cloud data. As 3D sensing becomes integral to modern manufacturing, reliable anomaly detection is essential for high-throughput quality assurance and process control. Yet practical deployment on resource-constrained, latency-critical systems remains limited. Existing methods are often computationally prohibitive or unreliable in complex, unmasked regions, and diffusion pipelines are inherently bottlenecked by iterative denoising. In this work, we address this bottleneck by reformulating reconstructionbased anomaly detection through consistency learning, enabling direct prediction of anomaly-free geometry in one or two network evaluations. We further introduce a novel hybrid loss formulation that explicitly enforces reconstruction toward clean data. This design substantially reduces inference cost, achieving up to 80x faster runtime than the current state-of-the-art method, without GPU acceleration, while preserving strong detection performance. It outperforms R3D-AD on Anomaly-ShapeNet with 76.20% I-AUROC and remains competitive on Real3DAD with 72.80% I-AUROC, enabling efficient, low-latency anomaly detection on resource-constrained platforms, including drones, smart industrial cameras, and other edge devices.