[2605.05372] Two Steps Are All You Need: Efficient 3D Point Cloud Anomaly Detection with Consistency Models
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.05372 (cs)
[Submitted on 6 May 2026]
Title:Two Steps Are All You Need: Efficient 3D Point Cloud Anomaly Detection with Consistency Models
Authors:Pranav A, Shashank B, Pranav Siddappa, Dominik Seuss, Minal Moharir, Subramanya KN
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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.
Comments: 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
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.05372 [cs.CV]
(or arXiv:2605.05372v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.05372
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arXiv-issued DOI via DataCite (pending registration)
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From: Pranav A [view email] [v1] Wed, 6 May 2026 18:52:53 UTC (2,769 KB)
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