Abstract: Through an investigation into point cloud object detection, it was found that current detection methods often require demanding supervised datasets, posing challenges in terms of manpower and resources. Therefore, an innovative solution is proposed, which involves the use of the Meta Learning framework to overcome the abundance of labeled data. Research is conducted on the application of Few-shot Learning techniques to 3D point cloud object detection. This approach can predict the classification of unlabeled samples based on a limited number of new class-labeled samples, thus achieving good results under limited data conditions. To achieve this, the Prototypical VoteNet is introduced to learn geometric prototypes of categories and support set prototypes. Additionally, an Intention-attention mechanism is introduced to learn point cloud context information for more precise information fusion. To avoid over-reliance on max-pooling and the loss of a significant amount of information in prototype generation, mean-pooling is used. Compared to baseline models on benchmark datasets, the method consistently demonstrates significant improvements, providing strong support for research and applications in the field of point cloud object detection.

摘要： 通过对点云目标检测的调查，发现现行的检测方法往往要求严苛的监督式数据集，这带来了人力、物力等方面的挑战。因此提出了一种创新性的解决方案，即采用元学习（Meta Learning）框架来克服大量标注数据的困扰。对小样本学习（Few-shot Learning）技术应用于3D点云目标检测进行研究。这一方法能够基于有限的新类别标注样本，预测未标注样本的分类，从而在有限数据条件下仍能取得良好效果。为此，引入了原型投票网络（Prototypical VoteNet）来学习类别的几何原型以及支持集的类别原型。此外，为了学习点云上下文信息，引入了意图注意力（Intention-attention）机制，以实现更加精准的信息融合。在原型生成方面，为避免点云原型过度依赖最大池化（Maxpool）而丧失大量信息，采用了平均池化（Meanpool）方法来生成原型。与基准数据集上的基线模型相比，方法呈现出显著且一致的提升效果，为点云目标检测领域的研究和应用提供了有力支持。