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<h1 class="title is-1 publication-title">Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Yuqi Cheng</a><sup>*1</sup>,</span>
<span class="author-block">
<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Yihan Sun</a><sup>*1</sup>,</span>
<span class="author-block">
<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Hui Zhang</a><sup>2</sup>,</span>
<span class="author-block">
<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Weiming Shen</a><sup>#1</sup>
<span class="author-block">
<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Yunkang Cao</a><sup>#2</sup>,</span>
</span>
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<span class="author-block">1 Huazhong University of Science and Technology, 2 Hunan University<br> AAAI 2026 Oral</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Equal Contribution    # Co-corresponding author</small></span>
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<h2 class="title is-3">Abstract</h2>
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<p>
In industrial point cloud analysis, detecting subtle anomalies demands high-resolution spatial data, yet prevailing benchmarks emphasize low-resolution inputs. To address this disparity, we propose a scalable pipeline for generating realistic and subtle 3D anomalies. Employing this pipeline, we developed MiniShift, the inaugural high-resolution 3D anomaly detection dataset, encompassing 2,577 point clouds, each with 500,000 points and anomalies occupying less than 1\% of the total. We further introduce Simple3D, an efficient framework integrating Multi-scale Neighborhood Descriptors (MSND) and Local Feature Spatial Aggregation (LFSA) to capture intricate geometric details with minimal computational overhead, achieving real-time inference exceeding 20 fps. Extensive evaluations on MiniShift and established benchmarks demonstrate that Simple3D surpasses state-of-the-art methods in both accuracy and speed, highlighting the pivotal role of high-resolution data and effective feature aggregation in advancing practical 3D anomaly detection.
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MiniShift Dataset
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Simple3D
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Results on MiniShift
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Visualization on MiniShift
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Results on Three Public Datasets
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Visualization on Three Public Datasets
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<h2 class="title">BibTeX</h2>
<pre><code>@article{MiniShift_Simple3D,
title={Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects},
author={Cheng, Yuqi and Sun, Yihan and Zhang, Hui and Shen, Weiming and Cao, Yunkang},
journal={arXiv preprint arXiv:2507.07435},
year={2025}
} </code></pre>
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