Moving deep learning models from the laboratory setting to the open world
entails preparing them to handle unforeseen conditions. In several applications
the occurrence of novel classes during deployment poses a significant threat,
thus it is crucial to effectively detect them. Ideally, this skill should be
used when needed without requiring any further computational training effort at
every new task. Out-of-distribution detection has attracted significant
attention in the last years, however the majority of the studies deal with 2D
images ignoring the inherent 3D nature of the real-world and often confusing
between domain and semantic novelty. In this work, we focus on the latter,
considering the objects geometric structure captured by 3D point clouds
regardless of the specific domain. We advance the field by introducing
OpenPatch that builds on a large pre-trained model and simply extracts from its
intermediate features a set of patch representations that describe each known
class. For any new sample, we obtain a novelty score by evaluating whether it
can be recomposed mainly by patches of a single known class or rather via the
contribution of multiple classes. We present an extensive experimental
evaluation of our approach for the task of semantic novelty detection on
real-world point cloud samples when the reference known data are synthetic. We
demonstrate that OpenPatch excels in both the full and few-shot known sample
scenarios, showcasing its robustness across varying pre-training objectives and
network backbones. The inherent training-free nature of our method allows for
its immediate application to a wide array of real-world tasks, offering a
compelling advantage over approaches that need expensive retraining efforts.

这篇论文介绍了 OpenPatch 方法，它使用预训练模型从中间特征中提取一组描述每个已知类别的补丁表示，在新样本中评估其是否主要由单个已知类别的补丁构成或通过多个类别的贡献来组成，并通过广泛的实验评估展示了其在语义新颖性检测任务中的出色性能。