论文标题
使用深几何描述符在3D点云中检测异常
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors
论文作者
论文摘要
我们提出了一种在高分辨率3D点云中无监督检测几何异常的新方法。特别是，我们建议将既定的学生老师探测框架适应三个维度。对学生网络进行了培训，以匹配在无异常点云上验证的教师网络的输出。当应用于测试数据时，教师和学生之间的回归错误允许对异常结构的可靠定位。为了构建一个提取密集的局部几何描述符的富有表现力的教师网络，我们介绍了一种新颖的自学预审策略。教师通过重建当地接受场而受到培训，不需要注释。对综合MVTEC 3D异常检测数据集进行了广泛的实验，突出了我们方法的有效性，该检测的有效性超过了下一方法，其差距很大。消融研究表明，我们的方法符合有关性能，运行时和内存消耗的实际应用要求。
We present a new method for the unsupervised detection of geometric anomalies in high-resolution 3D point clouds. In particular, we propose an adaptation of the established student-teacher anomaly detection framework to three dimensions. A student network is trained to match the output of a pretrained teacher network on anomaly-free point clouds. When applied to test data, regression errors between the teacher and the student allow reliable localization of anomalous structures. To construct an expressive teacher network that extracts dense local geometric descriptors, we introduce a novel self-supervised pretraining strategy. The teacher is trained by reconstructing local receptive fields and does not require annotations. Extensive experiments on the comprehensive MVTec 3D Anomaly Detection dataset highlight the effectiveness of our approach, which outperforms the next-best method by a large margin. Ablation studies show that our approach meets the requirements of practical applications regarding performance, runtime, and memory consumption.
