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.

翻译：基于RGB的表面异常检测方法已取得显著进展。然而，部分表面异常在仅依靠RGB图像时仍难以识别，因此需要引入三维信息。现有采用点云主干网络的方法存在表征欠优化及处理速度慢导致适用性降低的问题。针对工业深度数据集规模不足的瓶颈，重新训练设计用于快速密集输入处理的RGB主干网络受到限制。为攻克这些难题，我们做出多项贡献：(i) 提出新型深度感知离散自编码器（DADA）架构，该架构能够学习联合建模RGB与三维数据的通用离散潜在空间，用于三维表面异常检测；(ii) 通过引入模拟过程获取深度编码器中的信息性深度特征，解决工业深度数据集多样性匮乏的问题；(iii) 提出新的表面异常检测方法3DSR，在具有挑战性的MVTec3D异常检测基准测试中，该方法在精度和处理速度上均超越现有所有最先进技术。实验结果验证了我们方法的有效性和高效性，凸显了利用深度信息改进表面异常检测的潜力。