探索用于自监督点云理解的语义掩码自编码器
Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding
AI总结：
提出Semantic Masked Autoencoder，通过基于原型的组件语义建模模块和组件语义增强掩码策略，解决随机掩码无法捕获语义关系的问题，提升点云自监督理解性能。
摘要：
点云理解旨在从无标签数据中获取鲁棒且泛化的特征表示。基于掩码点建模的方法近期在各种下游任务中展现出显著性能。这些预训练方法依赖随机掩码策略，通过恢复受损的点云输入来建立对点云的感知，这导致自监督模型无法捕获合理的语义关系。为解决此问题，我们提出了Semantic Masked Autoencoder，它包含两个主要组件：一个基于原型的组件语义建模模块和一个组件语义增强的掩码策略。具体而言，在组件语义建模模块中，我们设计了一种组件语义引导机制，指导一组可学习的原型从物体中捕获不同组件的语义。利用这些原型，我们开发了一种组件语义增强的掩码策略，以解决随机掩码在有效覆盖完整组件结构方面的局限性。此外，我们引入了组件语义增强的提示微调策略，进一步利用这些原型来提升预训练模型在下游任务中的性能。在ScanObjectNN、ModelNet40和ShapeNetPart等数据集上进行的广泛实验证明了我们提出模块的有效性。
英文摘要：
Point cloud understanding aims to acquire robust and general feature representations from unlabeled data. Masked point modeling-based methods have recently shown significant performance across various downstream tasks. These pre-training methods rely on random masking strategies to establish the perception of point clouds by restoring corrupted point cloud inputs, which leads to the failure of capturing reasonable semantic relationships by the self-supervised models. To address this issue, we propose Semantic Masked Autoencoder, which comprises two main components: a prototype-based component semantic modeling module and a component semantic-enhanced masking strategy. Specifically, in the component semantic modeling module, we design a component semantic guidance mechanism to direct a set of learnable prototypes in capturing the semantics of different components from objects. Leveraging these prototypes, we develop a component semantic-enhanced masking strategy that addresses the limitations of random masking in effectively covering complete component structures. Furthermore, we introduce a component semantic-enhanced prompt-tuning strategy, which further leverages these prototypes to improve the performance of pre-trained models in downstream tasks. Extensive experiments conducted on datasets such as ScanObjectNN, ModelNet40, and ShapeNetPart demonstrate the effectiveness of our proposed modules.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
