3D点云中的持续学习：采用谱技术进行样本选择
Continual Learning in 3D Point Clouds: Employing Spectral Techniques for Exemplar Selection
AI总结：
提出基于谱聚类的持续学习框架CL3D，利用3D几何特征在多特征空间选择原型，在ModelNet40等数据集上以更低内存实现更高准确率。
摘要：
我们提出了一种用于3D物体分类持续学习的新框架。我们的方法CL3D基于使用谱聚类从每个类别中选择原型。对于像点云这样的非欧几里得数据，只要能定义样本对之间的距离度量，就可以采用谱聚类。选择合适的距离度量使我们能够利用3D几何特征来识别每个类别的代表性原型。我们探讨了在输入空间（3D点）、局部特征空间（1024维点）和全局特征空间中聚类的有效性。我们在ModelNet40、ShapeNet和ScanNet数据集上进行了实验，仅通过使用输入空间特征就实现了最先进的准确率。通过结合输入、局部和全局特征，我们改进了ModelNet和ShapeNet上的现有技术水平，且仅使用了竞争方法近一半的内存。对于具有挑战性的ScanNet数据集，我们的方法在仅消耗竞争对手28%内存的情况下，将准确率提高了4.1%，证明了我们方法的可扩展性。
英文摘要：
We introduce a novel framework for Continual Learning in 3D object classification. Our approach, CL3D, is based on the selection of prototypes from each class using spectral clustering. For non-Euclidean data such as point clouds, spectral clustering can be employed as long as one can define a distance measure between pairs of samples. Choosing the appropriate distance measure enables us to leverage 3D geometric characteristics to identify representative prototypes for each class. We explore the effectiveness of clustering in the input space (3D points), local feature space (1024-dimensional points), and global feature space. We conduct experiments on the ModelNet40, ShapeNet, and ScanNet datasets, achieving state-of-the-art accuracy exclusively through the use of input space features. By leveraging the combined input, local, and global features, we have improved the state-of-the-art on ModelNet and ShapeNet, utilizing nearly half the memory used by competing approaches. For the challenging ScanNet dataset, our method enhances accuracy by 4.1% while consuming just 28% of the memory used by our competitors, demonstrating the scalability of our approach.
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