3D object recognition has successfully become an appealing research topic in the real-world. However, most existing recognition models unreasonably assume that the categories of 3D objects cannot change over time in the real-world. This unrealistic assumption may result in significant performance degradation for them to learn new classes of 3D objects consecutively, due to the catastrophic forgetting on old learned classes. Moreover, they cannot explore which 3D geometric characteristics are essential to alleviate the catastrophic forgetting on old classes of 3D objects. To tackle the above challenges, we develop a novel Incremental 3D Object Recognition Network (i.e., InOR-Net), which could recognize new classes of 3D objects continuously via overcoming the catastrophic forgetting on old classes. Specifically, a category-guided geometric reasoning is proposed to reason local geometric structures with distinctive 3D characteristics of each class by leveraging intrinsic category information. We then propose a novel critic-induced geometric attention mechanism to distinguish which 3D geometric characteristics within each class are beneficial to overcome the catastrophic forgetting on old classes of 3D objects, while preventing the negative influence of useless 3D characteristics. In addition, a dual adaptive fairness compensations strategy is designed to overcome the forgetting brought by class imbalance, by compensating biased weights and predictions of the classifier. Comparison experiments verify the state-of-the-art performance of the proposed InOR-Net model on several public point cloud datasets.

翻译：三维物体识别已成为现实世界中一个引人关注的研究课题。然而，现有大多数识别模型不合理地假设三维物体类别在真实世界中不会随时间变化。由于对旧有学习类别的灾难性遗忘，这种不切实际的假设可能导致模型在连续学习新类别三维物体时性能显著下降。此外，这些模型无法探索哪些三维几何特征对于缓解旧类别三维物体的灾难性遗忘至关重要。为解决上述挑战，我们提出了一种新型增量式三维物体识别网络（即InOR-Net），该网络通过克服对旧类别的灾难性遗忘，能够持续识别新类别的三维物体。具体而言，我们提出了一种类别引导的几何推理方法，通过利用固有类别信息来推理具有各类别独特三维特征的局部几何结构。随后，我们提出了一种新颖的评论家引导的几何注意力机制，用于区分每个类别中哪些三维几何特征有助于克服对旧类别三维物体的灾难性遗忘，同时防止无用三维特征的负面影响。此外，我们设计了一种双自适应公平补偿策略，通过补偿分类器的偏置权重和预测来克服类别不平衡带来的遗忘问题。对比实验验证了所提出的InOR-Net模型在多个公开点云数据集上的最优性能。