2026/05/06 by Pranav A, Shashank B, Pranav Siddappa +4 · 1 voice
Computer Science · Engineering · #3D Shape Modeling and Analysis #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2605.05372
published as Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2026, pp. 3479-3487 · Accepted to CVPR 2026, at the 9th Workshop on Efficient Deep Learning for Computer Vision (ECV). To be published in the IEEE/CVF CVPR 2026 Workshop Proceedings
arxiv created 2026/05/06 · openalex publication_date 2026/05/06 · arxiv published 2026/05/06 · arxiv updated 2026/05/06 · openalex created_date 2026/05/09 · openalex updated_date 2026/07/28
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.