欺骗深度：通过深度模拟增强3D表面异常检测
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation
AI总结：
本文提出DADA架构与深度模拟流程，并构建3DSR方法，联合建模RGB与3D信息，在MVTec3D基准上以更高精度和速度实现3D表面异常检测。
摘要：
基于RGB的表面异常检测方法已取得显著进展。然而，某些表面异常在仅使用RGB时实际上仍不可见，因此需要纳入3D信息。现有采用点云骨干网络的方法因处理速度慢而存在表示欠佳和适用性降低的问题。为更快的密集输入处理而设计的RGB骨干网络，若在工业深度数据集上重新训练，则会受到缺乏足够大规模数据集的阻碍。为应对这些挑战，我们作出了若干贡献。（i）我们提出了一种新颖的深度感知离散自编码器（Depth-Aware Discrete Autoencoder，DADA）架构，它能够学习一个通用的离散潜在空间，联合建模RGB和3D数据，用于3D表面异常检测。（ii）我们通过引入一种模拟过程来解决多样化工业深度数据集不足的问题，以在深度编码器中学习信息丰富的深度特征。（iii）我们提出了一种新的表面异常检测方法3DSR，该方法在具有挑战性的MVTec3D异常检测基准上，无论在准确性还是处理速度方面，都优于所有现有最先进方法。实验结果验证了我们方法的有效性和高效性，突显了利用深度信息改进表面异常检测的潜力。
英文摘要：
RGB-based surface anomaly detection methods have advanced significantly. However, certain surface anomalies remain practically invisible in RGB alone, necessitating the incorporation of 3D information. Existing approaches that employ point-cloud backbones suffer from suboptimal representations and reduced applicability due to slow processing. Re-training RGB backbones, designed for faster dense input processing, on industrial depth datasets is hindered by the limited availability of sufficiently large datasets. We make several contributions to address these challenges. (i) We propose a novel Depth-Aware Discrete Autoencoder (DADA) architecture, that enables learning a general discrete latent space that jointly models RGB and 3D data for 3D surface anomaly detection. (ii) We tackle the lack of diverse industrial depth datasets by introducing a simulation process for learning informative depth features in the depth encoder. (iii) We propose a new surface anomaly detection method 3DSR, which outperforms all existing state-of-the-art on the challenging MVTec3D anomaly detection benchmark, both in terms of accuracy and processing speed. The experimental results validate the effectiveness and efficiency of our approach, highlighting the potential of utilizing depth information for improved surface anomaly detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
