标题： PointGAC：面向几何的代码本用于掩码点云建模 显示英文标题

标题： PointGAC: Geometric-Aware Codebook for Masked Point Cloud Modeling

摘要： 大多数遮罩点云建模（MPM）方法遵循回归范式来重建遮罩区域的坐标或特征。 然而，它们往往过度约束模型以学习遮罩区域的细节，导致无法捕捉泛化的特征。 为了解决这一限制，我们提出了\textbf{\textit{点GAC}}，一种新颖的基于聚类的 MPM 方法，旨在对齐遮罩区域的特征分布。 特别地，它具有一个在线代码本引导的师生框架。 首先，它提出了一种几何感知的分块策略以提取初始块。 然后，教师模型通过基于完整块特征的在线 k-means 更新代码本。 此过程促进了代码本向量成为聚类中心。 随后，我们将未遮罩的特征分配到其对应的聚类中心，并且学生模型对重建的遮罩特征的分配进行对齐。 这种策略专注于识别遮罩特征所属的聚类中心，使模型能够学习更泛化的特征表示。 得益于提出的代码本维护机制，代码本向量被主动更新，这进一步提高了语义特征学习的效率。 实验验证了所提方法在各种下游任务中的有效性。 代码可在 https://github.com/LAB123-tech/PointGAC 获得。 显示英文摘要

摘要： Most masked point cloud modeling (MPM) methods follow a regression paradigm to reconstruct the coordinate or feature of masked regions. However, they tend to over-constrain the model to learn the details of the masked region, resulting in failure to capture generalized features. To address this limitation, we propose \textbf{\textit{PointGAC}}, a novel clustering-based MPM method that aims to align the feature distribution of masked regions. Specially, it features an online codebook-guided teacher-student framework. Firstly, it presents a geometry-aware partitioning strategy to extract initial patches. Then, the teacher model updates a codebook via online k-means based on features extracted from the complete patches. This procedure facilitates codebook vectors to become cluster centers. Afterward, we assigns the unmasked features to their corresponding cluster centers, and the student model aligns the assignment for the reconstructed masked features. This strategy focuses on identifying the cluster centers to which the masked features belong, enabling the model to learn more generalized feature representations. Benefiting from a proposed codebook maintenance mechanism, codebook vectors are actively updated, which further increases the efficiency of semantic feature learning. Experiments validate the effectiveness of the proposed method on various downstream tasks. Code is available at https://github.com/LAB123-tech/PointGAC