In point cloud analysis, point-based methods have rapidly developed in recent
years. These methods have recently focused on concise MLP structures, such as
PointNeXt, which have demonstrated competitiveness with Convolutional and
Transformer structures. However, standard MLPs are limited in their ability to
extract local features effectively. To address this limitation, we propose a
Vector-oriented Point Set Abstraction that can aggregate neighboring features
through higher-dimensional vectors. To facilitate network optimization, we
construct a transformation from scalar to vector using independent angles based
on 3D vector rotations. Finally, we develop a PointVector model that follows
the structure of PointNeXt. Our experimental results demonstrate that
PointVector achieves state-of-the-art performance $\textbf{72.3\% mIOU}$ on the
S3DIS Area 5 and $\textbf{78.4\% mIOU}$ on the S3DIS (6-fold cross-validation)
with only $\textbf{58\%}$ model parameters of PointNeXt. We hope our work will
help the exploration of concise and effective feature representations. The code
will be released soon.

本文探讨了基于点云的方法在点云分析中的应用，提出了一种聚合邻近特征的高维向量的 “Vector-oriented Point Set Abstraction” 方法，并开发了一种 PointNeXt 结构的 PointVector 模型以提高特征提取的效率，此方法在 S3DIS Area 5 上和 S3DIS (6 折交叉验证) 上取得了 72.3% 和 78.4% 的 mIOU 的优异表现，在研究中探索了更加紧凑和有效的特征表示方法。